title
1. ChatGPT Prompt Engineering for Developers | Andrew Ng | DeepLearning.ai - Full Course

description
This lesson comes from [https://learn.deeplearning.ai/reinforcement-learning-from-human-feedback] (https://learn.deeplearning.ai/reinforcement-learning-from-human-feedback) This video course introduces ChatGPT Prompt Engineering for Developers. The course will be led by Andrew Ng The keynote speaker shared best practices for ChatGPT prompt engineering with OpenAI Technical team member Iza Fulford. The course emphasizes Large Language models (LLMS) as a developer tool, calling LLMS through apis to quickly build software applications. Two types of LLM are highlighted: Basic LLM and Instruction Adjusted LLM. Tuning LLM with instructions is recommended because it is easier to use and more secure. The video also explores the principles of cue engineering, including making specific instructions clear and giving the model enough time to think. Get free course notes: https://t.me/NoteForYoutubeCourse

detail
{'title': '1. ChatGPT Prompt Engineering for Developers | Andrew Ng | DeepLearning.ai - Full Course', 'heatmap': [], 'summary': 'The course delves into chatgpt prompt engineering, emphasizing the potential of large language models for developers, discussing the shift towards using instruction-tuned llms, and tactics for optimizing model performance, including iterative prompt development and efficient text summarization techniques.', 'chapters': [{'end': 174.344, 'segs': [{'end': 67.71, 'src': 'embed', 'start': 44.934, 'weight': 0, 'content': [{'end': 54.36, 'text': 'A lot of that has been focused on the ChatGPT web user interface, which many people are using to do specific and often one-off tasks.', 'start': 44.934, 'duration': 9.426}, {'end': 64.947, 'text': 'But I think the power of LLMs large language models as a developer tool that is, using API calls to LLMs to quickly build software applications.', 'start': 55.02, 'duration': 9.927}, {'end': 67.71, 'text': 'I think that is still very underappreciated.', 'start': 64.947, 'duration': 2.763}], 'summary': 'Chatgpt web ui is popular for specific tasks, but api calls to llms for software dev is underappreciated.', 'duration': 22.776, 'max_score': 44.934, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k44934.jpg'}, {'end': 131.817, 'src': 'embed', 'start': 85.904, 'weight': 1, 'content': [{'end': 93.626, 'text': "So in this course, we'll share with you some of the possibilities for what you can do, as well as best practices for how you can do them.", 'start': 85.904, 'duration': 7.722}, {'end': 96.366, 'text': "There's a lot of material to cover.", 'start': 94.846, 'duration': 1.52}, {'end': 100.908, 'text': "First, you'll learn some prompting best practices for software development.", 'start': 97.027, 'duration': 3.881}, {'end': 106.829, 'text': "Then we'll cover some common use cases, summarizing, inferring, transforming, expanding.", 'start': 101.248, 'duration': 5.581}, {'end': 109.29, 'text': "And then you'll build a chatbot using an LLM.", 'start': 107.149, 'duration': 2.141}, {'end': 114.326, 'text': 'We hope that this will spark your imagination about new applications that you can build.', 'start': 110.424, 'duration': 3.902}, {'end': 120.71, 'text': 'So, in the development of large language models or LLMs, there have been broadly two types of LLMs,', 'start': 115.327, 'duration': 5.383}, {'end': 125.233, 'text': "which I'm going to refer to as base LLMs and instruction-tuned LLMs.", 'start': 120.71, 'duration': 4.523}, {'end': 131.817, 'text': 'So base LLM has been trained to predict the next word based on text training data,', 'start': 126.154, 'duration': 5.663}], 'summary': 'Course covers best practices for software development and building a chatbot using an llm.', 'duration': 45.913, 'max_score': 85.904, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k85904.jpg'}], 'start': 5.256, 'title': 'Chatgpt prompt engineering', 'summary': 'Discusses the potential of large language models (llms) as a developer tool, different types of llms, and the importance of using api calls to llms for quickly building software applications.', 'chapters': [{'end': 174.344, 'start': 5.256, 'title': 'Chatgpt prompt engineering', 'summary': 'Discusses the potential of large language models (llms) as a developer tool, the different types of llms, and the course content, emphasizing the importance of using api calls to llms for quickly building software applications.', 'duration': 169.088, 'highlights': ['The importance of using API calls to LLMs for quickly building software applications The power of LLMs as a developer tool lies in using API calls to LLMs for quickly building software applications, which is still underappreciated.', 'Differentiating between base LLMs and instruction-tuned LLMs The chapter explains the two types of LLMs: base LLMs, trained to predict the next word based on text training data, and instruction-tuned LLMs, providing examples of their predictive capabilities.', 'Course content and emphasis on prompting best practices for software development The course covers prompting best practices for software development, common use cases like summarizing, inferring, transforming, and expanding, and building a chatbot using an LLM to spark imagination about new applications.']}], 'duration': 169.088, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k5256.jpg', 'highlights': ['The importance of using API calls to LLMs for quickly building software applications', 'Differentiating between base LLMs and instruction-tuned LLMs', 'Course content and emphasis on prompting best practices for software development']}, {'end': 734.193, 'segs': [{'end': 271.602, 'src': 'embed', 'start': 241.306, 'weight': 0, 'content': [{'end': 246.411, 'text': 'Some of the best practices you find on the Internet may be more suited for a base LLM,', 'start': 241.306, 'duration': 5.105}, {'end': 256.1, 'text': 'but for most practical applications today we would recommend most people instead focus on instruction-tuned LLMs, which are easier to use and also,', 'start': 246.411, 'duration': 9.689}, {'end': 261.704, 'text': 'because of the work of OpenAI and other LLM companies, becoming safer and more aligned.', 'start': 256.1, 'duration': 5.604}, {'end': 271.602, 'text': 'So this course will focus on best practices for instruction-tuned OEMs, which is what we recommend you use for most of your applications.', 'start': 262.856, 'duration': 8.746}], 'summary': 'Focus on instruction-tuned llms for most practical applications.', 'duration': 30.296, 'max_score': 241.306, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k241306.jpg'}, {'end': 398.232, 'src': 'embed', 'start': 369.202, 'weight': 3, 'content': [{'end': 377.392, 'text': 'So in the next video, you see examples of how to be clear and specific, which is an important principle of prompting LLMs.', 'start': 369.202, 'duration': 8.19}, {'end': 384.4, 'text': 'And, uh, you also learn from Iser a second principle of prompting that is giving a DLM time to think.', 'start': 377.932, 'duration': 6.468}, {'end': 387.143, 'text': "So with that, let's go on to the next video.", 'start': 384.98, 'duration': 2.163}, {'end': 398.232, 'text': 'In this video, Iza will present some guidelines for prompting to help you get the results that you want.', 'start': 392.99, 'duration': 5.242}], 'summary': 'Principles of prompting llms: clarity, specificity, and giving dlm time to think, presented in next video.', 'duration': 29.03, 'max_score': 369.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k369202.jpg'}, {'end': 647.396, 'src': 'embed', 'start': 621.077, 'weight': 5, 'content': [{'end': 627.842, 'text': 'So if we run this, As you can see,', 'start': 621.077, 'duration': 6.765}, {'end': 637.309, 'text': "we've received a sentence output and we've used these delimiters to make it very clear to the model kind of the exact text it should summarize.", 'start': 627.842, 'duration': 9.467}, {'end': 644.534, 'text': 'So delimiters can be kind of any clear punctuation that separates specific pieces of text from the rest of the prompt.', 'start': 638.17, 'duration': 6.364}, {'end': 647.396, 'text': 'These could be kind of triple backticks.', 'start': 644.915, 'duration': 2.481}], 'summary': 'Using delimiters to specify text for model summarization.', 'duration': 26.319, 'max_score': 621.077, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k621077.jpg'}, {'end': 716.311, 'src': 'embed', 'start': 686.633, 'weight': 4, 'content': [{'end': 690.595, 'text': 'Because we have these delimiters, the model kind of knows that this is the text that should summarize,', 'start': 686.633, 'duration': 3.962}, {'end': 694.558, 'text': 'and it should just actually summarize these instructions rather than following them itself.', 'start': 690.595, 'duration': 3.963}, {'end': 698.42, 'text': 'The next tactic is to ask for a structured output.', 'start': 695.138, 'duration': 3.282}, {'end': 706.205, 'text': 'So to make parsing the model outputs easier, it can be helpful to ask for a structured output like HTML or JSON.', 'start': 699.901, 'duration': 6.304}, {'end': 708.746, 'text': 'So let me copy another example over.', 'start': 706.665, 'duration': 2.081}, {'end': 716.311, 'text': "So in the prompt we're saying generate a list of three made up book titles along with their authors and genres.", 'start': 709.567, 'duration': 6.744}], 'summary': 'Using delimiters, the model summarizes text instructions and produces structured outputs like html or json.', 'duration': 29.678, 'max_score': 686.633, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k686633.jpg'}], 'start': 175.703, 'title': 'Instruction-tuned llms and effectively prompting llms', 'summary': 'Discusses the shift towards using instruction-tuned llms, emphasizing their practical usage scenarios and the principles of effectively prompting language models, highlighting the importance of clear and specific instructions, delimiters, and structured output for guiding the model towards the desired output.', 'chapters': [{'end': 300.831, 'start': 175.703, 'title': 'Instruction-tuned llms', 'summary': 'Discusses the shift towards using instruction-tuned llms, which are trained to follow instructions and are safer and more aligned, with practical usage scenarios moving towards their usage due to their ability to be helpful, honest, and harmless.', 'duration': 125.128, 'highlights': ['Instruction-tuned LLMs are trained to be helpful, honest, and harmless. Instruction-tuned LLMs are less likely to output problematic texts such as toxic outputs compared to base LLMs, making them safer and more aligned.', 'Practical usage scenarios shifting towards instruction-tuned LLMs. Due to their ability to be helpful, honest, and harmless, practical usage scenarios have been shifting towards the usage of instruction-tuned LLMs, making them more suitable for most practical applications.', 'Best practices for using instruction-tuned LLMs recommended for most applications. The course focuses on best practices for using instruction-tuned LLMs, recommending their usage for most applications due to their ease of use and increased safety.']}, {'end': 734.193, 'start': 301.511, 'title': 'Effectively prompting llms', 'summary': 'Discusses the principles of effectively prompting language models, emphasizing the importance of clear and specific instructions and providing examples of using delimiters and structured output to guide the model towards the desired output.', 'duration': 432.682, 'highlights': ['The chapter discusses the principles of effectively prompting Language Models, emphasizing the importance of clear and specific instructions. Principles of effectively prompting LLMs are highlighted, emphasizing the importance of clear and specific instructions to guide the model towards the desired output.', 'Examples of using delimiters and structured output to guide the model towards the desired output are provided. The chapter provides examples of using delimiters and structured output, such as JSON format, to guide the model towards the desired output.', 'The importance of using delimiters to clearly indicate distinct parts of the input is emphasized. The chapter emphasizes the importance of using delimiters, such as triple backticks, to clearly indicate distinct parts of the input and guide the model effectively.']}], 'duration': 558.49, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k175703.jpg', 'highlights': ['Instruction-tuned LLMs are less likely to output problematic texts compared to base LLMs, making them safer and more aligned.', 'Practical usage scenarios have been shifting towards the usage of instruction-tuned LLMs, making them more suitable for most practical applications.', 'Best practices for using instruction-tuned LLMs recommended for most applications due to their ease of use and increased safety.', 'Principles of effectively prompting LLMs are highlighted, emphasizing the importance of clear and specific instructions to guide the model towards the desired output.', 'The chapter provides examples of using delimiters and structured output, such as JSON format, to guide the model towards the desired output.', 'The chapter emphasizes the importance of using delimiters, such as triple backticks, to clearly indicate distinct parts of the input and guide the model effectively.']}, {'end': 1372.449, 'segs': [{'end': 785.503, 'src': 'embed', 'start': 755.581, 'weight': 2, 'content': [{'end': 760.643, 'text': "if they're not satisfied, indicate this and kind of stop short of a full task completion attempt.", 'start': 755.581, 'duration': 5.062}, {'end': 768.107, 'text': 'You might also consider potential edge cases and how the model should handle them to avoid unexpected errors or result.', 'start': 761.763, 'duration': 6.344}, {'end': 774.791, 'text': 'So now I will copy over a paragraph and this is just a paragraph describing the steps to make a cup of tea.', 'start': 768.828, 'duration': 5.963}, {'end': 778.434, 'text': 'And then I will copy over our prompt.', 'start': 775.932, 'duration': 2.502}, {'end': 785.503, 'text': "And so the prompt is, you'll be provided with text delimited by triple quotes.", 'start': 781.96, 'duration': 3.543}], 'summary': 'Consider potential edge cases for model to avoid errors.', 'duration': 29.922, 'max_score': 755.581, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k755581.jpg'}, {'end': 871.19, 'src': 'embed', 'start': 844.683, 'weight': 0, 'content': [{'end': 853.547, 'text': 'and this is just providing examples of successful executions of the task you want performed before asking the model to do the actual task you want it to do.', 'start': 844.683, 'duration': 8.864}, {'end': 855.388, 'text': 'So let me show you an example.', 'start': 854.147, 'duration': 1.241}, {'end': 863.762, 'text': "So in this prompt, we're telling the model that its task is to answer in a consistent style.", 'start': 858.877, 'duration': 4.885}, {'end': 871.19, 'text': 'And so we have this example of a kind of conversation between a child and a grandparent.', 'start': 864.423, 'duration': 6.767}], 'summary': 'Demonstrating successful task executions before asking the model to perform the actual task.', 'duration': 26.507, 'max_score': 844.683, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k844683.jpg'}, {'end': 946.946, 'src': 'embed', 'start': 920.49, 'weight': 1, 'content': [{'end': 926.653, 'text': 'you should try reframing the query to request a chain or series of relevant reasoning before the model provides its final answer.', 'start': 920.49, 'duration': 6.163}, {'end': 935.278, 'text': "Another way to think about this is that if you give a model a task that's too complex for it to do in a short amount of time or in a small number of words,", 'start': 927.254, 'duration': 8.024}, {'end': 937.66, 'text': 'it may make up a guess which is likely to be incorrect.', 'start': 935.278, 'duration': 2.382}, {'end': 940.041, 'text': 'And you know, this would happen for a person too.', 'start': 938.3, 'duration': 1.741}, {'end': 946.946, 'text': 'If you ask someone to complete a complex math question without time to work out the answer first, they would also likely make a mistake.', 'start': 940.461, 'duration': 6.485}], 'summary': 'Complex tasks may lead to incorrect guesses, applies to people too.', 'duration': 26.456, 'max_score': 920.49, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k920490.jpg'}, {'end': 1101.683, 'src': 'embed', 'start': 1070.694, 'weight': 4, 'content': [{'end': 1074.055, 'text': 'So text, summary, translation, names, and output JSON.', 'start': 1070.694, 'duration': 3.361}, {'end': 1080.977, 'text': 'And then we start by just saying the text to summarize, or we can even just say text.', 'start': 1074.736, 'duration': 6.241}, {'end': 1084.278, 'text': 'And then this is the same text as before.', 'start': 1082.638, 'duration': 1.64}, {'end': 1087.379, 'text': "So let's run this.", 'start': 1086.699, 'duration': 0.68}, {'end': 1095.12, 'text': 'So as you can see, this is the completion and the model has used the format that we asked for.', 'start': 1090.037, 'duration': 5.083}, {'end': 1101.683, 'text': "So we already gave it the text and then it's given us the summary, the translation, the names, and the output JSON.", 'start': 1095.46, 'duration': 6.223}], 'summary': 'The model provided summary, translation, names, and output json for the given text.', 'duration': 30.989, 'max_score': 1070.694, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1070694.jpg'}, {'end': 1361.369, 'src': 'embed', 'start': 1333.097, 'weight': 6, 'content': [{'end': 1339.119, 'text': 'This means that it might try to answer questions about obscure topics and can make things up that sound plausible but are not actually true.', 'start': 1333.097, 'duration': 6.022}, {'end': 1342.321, 'text': 'And we call these fabricated ideas hallucinations.', 'start': 1339.6, 'duration': 2.721}, {'end': 1348.196, 'text': "And so I'm going to show you an example of a case where the model will hallucinate something.", 'start': 1343.612, 'duration': 4.584}, {'end': 1355.183, 'text': 'This is an example of where the model confabulates a description of a made-up product name from a real toothbrush company.', 'start': 1348.737, 'duration': 6.446}, {'end': 1361.369, 'text': 'So the prompt is, tell me about AeroGlide Ultra Slim Smart Toothbrush by Boy.', 'start': 1355.623, 'duration': 5.746}], 'summary': 'Model can create fabricated descriptions, like aeroglide ultra slim smart toothbrush by boy.', 'duration': 28.272, 'max_score': 1333.097, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1333097.jpg'}], 'start': 734.473, 'title': 'Optimizing model performance and limitations', 'summary': 'Delves into tactics for optimizing model performance like clear instructions, allowing reasoning time, handling edge cases, and using few-shot prompting. it also covers instructing the model, allowing it to reason out solutions, and discussing limitations such as fabricating incorrect information.', 'chapters': [{'end': 960.174, 'start': 734.473, 'title': 'Optimizing model performance', 'summary': 'Discusses tactics for optimizing model performance including giving clear and specific instructions, allowing time for reasoning, handling edge cases, and using few-shot prompting.', 'duration': 225.701, 'highlights': ['Give clear and specific instructions to the model to avoid assumptions and errors, as demonstrated by providing successful task execution examples. One tactic is to ask the model to check whether conditions are satisfied and consider potential edge cases to avoid unexpected errors or results.', 'Allow the model time to think and reason by reframing queries to request a series of relevant reasoning before providing a final answer, similar to allowing a person time to solve a complex problem. Reframe queries to request a chain or series of relevant reasoning before the model provides its final answer to avoid rushing to an incorrect conclusion.', 'Handling edge cases and unexpected errors by instructing the model to check assumptions and avoid unexpected errors or results. Instruct the model to handle potential edge cases and check assumptions to avoid unexpected errors or results.', 'Utilize few-shot prompting by providing examples of successful task execution to guide the model in performing the desired task. Provide examples of successful task execution to guide the model in performing the desired task before asking it to do the actual task.']}, {'end': 1372.449, 'start': 961.014, 'title': 'Model instruction and limitations', 'summary': 'Discusses instructing the model to follow specific steps, allowing it time to reason out solutions, and the limitations of the model such as fabricating plausible but incorrect information.', 'duration': 411.435, 'highlights': ['Model instructed to follow specific steps for task completion The chapter discusses instructing the model to follow specific steps, such as summarizing text, translating it, listing names, and outputting a JSON object, to complete a task.', 'Model instructed to reason out solutions before rushing to a conclusion The chapter explains that instructing the model to reason out solutions before rushing to conclusions can lead to more accurate responses, as demonstrated in a math problem where the model initially agreed with an incorrect student solution.', 'Limitations of the model in memorizing and boundary of knowledge The chapter highlights the limitations of the model in memorizing information and knowing the boundary of its knowledge, leading to fabricated ideas and hallucinations when answering questions about obscure topics.']}], 'duration': 637.976, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k734473.jpg', 'highlights': ['Give clear and specific instructions to the model to avoid assumptions and errors, as demonstrated by providing successful task execution examples.', 'Allow the model time to think and reason by reframing queries to request a series of relevant reasoning before providing a final answer.', 'Handling edge cases and unexpected errors by instructing the model to check assumptions and avoid unexpected errors or results.', 'Utilize few-shot prompting by providing examples of successful task execution to guide the model in performing the desired task.', 'Model instructed to follow specific steps for task completion, such as summarizing text, translating it, listing names, and outputting a JSON object.', 'Model instructed to reason out solutions before rushing to a conclusion, leading to more accurate responses.', 'Limitations of the model in memorizing information and knowing the boundary of its knowledge, leading to fabricated ideas and hallucinations when answering questions about obscure topics.']}, {'end': 1844.055, 'segs': [{'end': 1416.546, 'src': 'embed', 'start': 1373.149, 'weight': 0, 'content': [{'end': 1377.353, 'text': 'And the reason that this can be kind of dangerous is that this actually sounds pretty realistic.', 'start': 1373.149, 'duration': 4.204}, {'end': 1385.819, 'text': "So make sure to use some of the techniques that we've gone through in this notebook to try and avoid this when you're building your own applications.", 'start': 1378.371, 'duration': 7.448}, {'end': 1391.926, 'text': "And this is a known weakness of the models and something that we're actively working on combating.", 'start': 1386.44, 'duration': 5.486}, {'end': 1400.295, 'text': 'And one additional tactic to reduce hallucinations, in the case that you want the model to generate answers based on a text,', 'start': 1392.526, 'duration': 7.769}, {'end': 1408.561, 'text': 'is to ask the model to first find any relevant quotes from the text and then ask it to use those quotes to kind of answer questions,', 'start': 1400.875, 'duration': 7.686}, {'end': 1416.546, 'text': 'and kind of having a way to trace the answer back to the source document is often pretty helpful to kind of reduce these hallucinations.', 'start': 1408.561, 'duration': 7.985}], 'summary': 'Models can generate realistic-sounding answers, but techniques can reduce hallucinations. weakness actively addressed.', 'duration': 43.397, 'max_score': 1373.149, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1373149.jpg'}, {'end': 1469.884, 'src': 'embed', 'start': 1443.175, 'weight': 2, 'content': [{'end': 1447.999, 'text': 'As long as you have a good process to iteratively make your prompt better,', 'start': 1443.175, 'duration': 4.824}, {'end': 1451.702, 'text': "then you'll be able to come to something that works well for the task you want to achieve.", 'start': 1447.999, 'duration': 3.703}, {'end': 1457.701, 'text': 'You may have heard me say that when I train a machine learning model, it almost never works the first time.', 'start': 1453.26, 'duration': 4.441}, {'end': 1460.822, 'text': "In fact, I'm very surprised if the first model I train works.", 'start': 1457.761, 'duration': 3.061}, {'end': 1467.003, 'text': 'I think with prompting, the odds of it working the first time is maybe a little bit higher, but, as Isa is saying,', 'start': 1461.422, 'duration': 5.581}, {'end': 1469.884, 'text': "it doesn't matter if the first prompt works.", 'start': 1467.003, 'duration': 2.881}], 'summary': 'Iterative process key to improving prompt success in machine learning models.', 'duration': 26.709, 'max_score': 1443.175, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1443175.jpg'}, {'end': 1701.089, 'src': 'embed', 'start': 1669.983, 'weight': 4, 'content': [{'end': 1676.986, 'text': 'So my prompt here says your task is to help a marketing team create a description for retail website or product based on a technical fact sheet,', 'start': 1669.983, 'duration': 7.003}, {'end': 1678.687, 'text': 'write a product description and so on.', 'start': 1676.986, 'duration': 1.701}, {'end': 1684.41, 'text': 'So this is my first attempt to explain the task to the large language model.', 'start': 1679.227, 'duration': 5.183}, {'end': 1688.872, 'text': 'So let me hit shift enter, and this takes a few seconds to run.', 'start': 1685.09, 'duration': 3.782}, {'end': 1692.524, 'text': 'and we get this result.', 'start': 1691.283, 'duration': 1.241}, {'end': 1701.089, 'text': "It looks like it's done a nice job writing a description, introducing a stunning mid-century inspired office chair, perfect edition, and so on.", 'start': 1693.184, 'duration': 7.905}], 'summary': 'Assisting marketing team in creating product descriptions based on technical fact sheets.', 'duration': 31.106, 'max_score': 1669.983, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1669983.jpg'}, {'end': 1830.792, 'src': 'embed', 'start': 1803.759, 'weight': 5, 'content': [{'end': 1809.606, 'text': "But these are different ways to tell the large language model what's the length of the output that you want.", 'start': 1803.759, 'duration': 5.847}, {'end': 1812.845, 'text': 'So this is one, two, three.', 'start': 1809.786, 'duration': 3.059}, {'end': 1813.965, 'text': 'I count three sentences.', 'start': 1812.965, 'duration': 1}, {'end': 1815.826, 'text': 'Looks like I did a pretty good job.', 'start': 1814.005, 'duration': 1.821}, {'end': 1823.109, 'text': "And then I've also seen people sometimes do things like, I don't know, use at most 280 characters.", 'start': 1816.566, 'duration': 6.543}, {'end': 1830.792, 'text': "Large language models, because of the way they interpret text, using something called a tokenizer, which I won't talk about.", 'start': 1824.149, 'duration': 6.643}], 'summary': 'The large language model can generate output of specified length, with an example of three sentences and 280 characters.', 'duration': 27.033, 'max_score': 1803.759, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1803759.jpg'}], 'start': 1373.149, 'title': 'Ai model weakness and tactics, iterative prompt development, and using language models to summarize product descriptions', 'summary': 'Discusses the dangers of ai-generated content, iterative prompt development, and using language models to summarize product descriptions, including tactics to reduce ai model weaknesses, iterative prompt refinement, and achieving a concise product description of 52 words and 281 characters.', 'chapters': [{'end': 1416.546, 'start': 1373.149, 'title': 'Ai model weakness and tactics', 'summary': 'Discusses the dangers of realistic-sounding ai-generated content, emphasizing the weakness of models and tactics to reduce hallucinations, including using relevant quotes to trace answers back to the source document.', 'duration': 43.397, 'highlights': ['One tactic to reduce hallucinations is to ask the model to find relevant quotes from the text and use those quotes to answer questions, helping to trace the answer back to the source document.', 'The danger of realistic-sounding AI-generated content is highlighted, emphasizing the need to use techniques to avoid it when building applications.']}, {'end': 1609.451, 'start': 1417.547, 'title': 'Iterative prompt development', 'summary': 'Highlights the importance of iteratively developing prompts for applications, emphasizing the need for a good process to refine prompts, and compares the process to iterative machine learning development.', 'duration': 191.904, 'highlights': ['The process of iteratively making prompts better is emphasized, with the focus on refining prompts until they work well for the specific task, similar to the iterative process in machine learning development.', 'The speaker mentions the likelihood of the first prompt working, but stresses that what matters most is the process for getting to a prompt that works for the application.', 'The comparison is drawn between the iterative process of developing prompts and the iterative process of machine learning model development, emphasizing the need for refining ideas and implementation to achieve effective outcomes.']}, {'end': 1844.055, 'start': 1610.712, 'title': 'Using language models to summarize product descriptions', 'summary': 'Discusses using a large language model to summarize a fact sheet for a chair, instructing it to create a product description with specific word count and length, eventually achieving a concise product description of 52 words and 281 characters.', 'duration': 233.343, 'highlights': ['The large language model is instructed to create a product description from a fact sheet for a chair, aiming for a concise and specific word count and length, eventually producing a description of 52 words and 281 characters.', 'The initial attempt at creating a product description resulted in a lengthy output, prompting the need to clarify the prompt to achieve a more concise description of the product.', 'Various instructions, including limiting the output to three sentences or at most 280 characters, are explored to guide the large language model in producing a product description of the desired length and content.']}], 'duration': 470.906, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1373149.jpg', 'highlights': ['The danger of realistic-sounding AI-generated content is highlighted, emphasizing the need to use techniques to avoid it when building applications.', 'One tactic to reduce hallucinations is to ask the model to find relevant quotes from the text and use those quotes to answer questions, helping to trace the answer back to the source document.', 'The comparison is drawn between the iterative process of developing prompts and the iterative process of machine learning model development, emphasizing the need for refining ideas and implementation to achieve effective outcomes.', 'The process of iteratively making prompts better is emphasized, with the focus on refining prompts until they work well for the specific task, similar to the iterative process in machine learning development.', 'The large language model is instructed to create a product description from a fact sheet for a chair, aiming for a concise and specific word count and length, eventually producing a description of 52 words and 281 characters.', 'Various instructions, including limiting the output to three sentences or at most 280 characters, are explored to guide the large language model in producing a product description of the desired length and content.', 'The initial attempt at creating a product description resulted in a lengthy output, prompting the need to clarify the prompt to achieve a more concise description of the product.']}, {'end': 2099.859, 'segs': [{'end': 1900.723, 'src': 'embed', 'start': 1869.844, 'weight': 0, 'content': [{'end': 1874.848, 'text': "it's actually intended to sell furniture to furniture retailers.", 'start': 1869.844, 'duration': 5.004}, {'end': 1879.951, 'text': 'that would be more interested in the technical details of the chair and the materials of the chair.', 'start': 1874.848, 'duration': 5.103}, {'end': 1889.058, 'text': 'In that case, you can take this prompt and say, I want to modify this prompt to get it to be more precise about the technical details.', 'start': 1880.612, 'duration': 8.446}, {'end': 1893.061, 'text': 'So let me keep on modifying this prompt.', 'start': 1890.24, 'duration': 2.821}, {'end': 1900.723, 'text': "And I'm going to say this description is intended for furniture retailers, so should be technical and focus on materials,", 'start': 1894.821, 'duration': 5.902}], 'summary': 'The description is tailored for furniture retailers, emphasizing technical details and materials.', 'duration': 30.879, 'max_score': 1869.844, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1869844.jpg'}, {'end': 1955.747, 'src': 'embed', 'start': 1925.525, 'weight': 3, 'content': [{'end': 1936.012, 'text': 'And when I look at this, I might decide at the end of the description, I also wanted to include the product IDs.', 'start': 1925.525, 'duration': 10.487}, {'end': 1941.656, 'text': 'So there are two offerings of this chair, SWC 110, SOC 100.', 'start': 1936.072, 'duration': 5.584}, {'end': 1946.24, 'text': 'So maybe I can further improve this prompt.', 'start': 1941.656, 'duration': 4.584}, {'end': 1949.762, 'text': 'And to get it to give me the product IDs,', 'start': 1947.861, 'duration': 1.901}, {'end': 1955.747, 'text': 'I can add this instruction at the end of the description include every seven character product ID in the technical specification.', 'start': 1949.762, 'duration': 5.985}], 'summary': 'The chair has two offerings, swc 110 and soc 100, with a plan to include product ids in the description.', 'duration': 30.222, 'max_score': 1925.525, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1925525.jpg'}, {'end': 2046.278, 'src': 'embed', 'start': 2004.438, 'weight': 1, 'content': [{'end': 2015.884, 'text': "it's worthwhile to often take a first attempt at writing a prompt see what happens and then go from there to iteratively refine the prompt to get closer and closer to the results that you need.", 'start': 2004.438, 'duration': 11.446}, {'end': 2025.713, 'text': 'And so a lot of the successful prompts that you may see using various programs was arrived at an iterative process like this.', 'start': 2017.631, 'duration': 8.082}, {'end': 2036.476, 'text': 'Just for fun, let me show you an example of an even more complex prompt that might give you a sense of what ChatGPT can do, which is.', 'start': 2026.713, 'duration': 9.763}, {'end': 2039.036, 'text': "I've just added a few extra instructions here.", 'start': 2036.476, 'duration': 2.56}, {'end': 2044.998, 'text': "After the description, include a table that gives the product dimensions, and then you'll format everything as HTML.", 'start': 2039.576, 'duration': 5.422}, {'end': 2046.278, 'text': "So let's run that.", 'start': 2045.338, 'duration': 0.94}], 'summary': 'Iterative refinement is key in successful prompt creation for better results. example of a complex prompt for chatgpt demonstrated.', 'duration': 41.84, 'max_score': 2004.438, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2004438.jpg'}], 'start': 1844.135, 'title': 'Ai prompt development', 'summary': 'Covers iterative prompt development to control output length and creating effective prompts for ai, with examples of refinement leading to successful prompt generation and testing complex prompts with chatgpt.', 'chapters': [{'end': 1986.095, 'start': 1844.135, 'title': 'Iterative prompt development', 'summary': 'Discusses iterative prompt development to control the output length by modifying prompts, resulting in a more precise and focused description, such as for furniture retailers, and including specific characteristics and product ids.', 'duration': 141.96, 'highlights': ['By modifying prompts, the output can be made more precise and focused, catering to specific audiences and including specific characteristics and product IDs.', 'Iterative prompt development allows for refining the text to cater to different audiences, such as furniture retailers, and including technical details and product IDs.', 'The example demonstrates modifying prompts to focus on specific characteristics desired in the output, resulting in a more tailored description with specific product IDs included.', 'The iterative prompt development process enables developers to control the output length and content precision, as seen in the example with tailored descriptions for specific audiences and product details included.']}, {'end': 2099.859, 'start': 1987.148, 'title': 'Writing effective ai prompts', 'summary': 'Discusses the importance of iterative refinement in creating effective prompts for ai, showcasing an example of an iterative process leading to successful prompt generation and testing a more complex prompt to demonstrate the capabilities of chatgpt.', 'duration': 112.711, 'highlights': ['Iterative refinement is crucial for creating successful prompts, as demonstrated by the process of refining a prompt to achieve desired results.', 'Showcasing an example of an iterative process leading to successful prompt generation exemplifies the importance of refining prompts for optimal outcomes.', 'Testing a more complex prompt to demonstrate the capabilities of ChatGPT provides insight into the potential of the AI model.', 'Emphasizing the need for clarity and specificity in prompts highlights the key factors for effective prompt creation.', 'Discussing the importance of allowing the model time to think emphasizes a crucial best practice for prompt generation.']}], 'duration': 255.724, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k1844135.jpg', 'highlights': ['Iterative prompt development allows for refining the text to cater to different audiences, such as furniture retailers, and including technical details and product IDs.', 'The iterative prompt development process enables developers to control the output length and content precision, as seen in the example with tailored descriptions for specific audiences and product details included.', 'By modifying prompts, the output can be made more precise and focused, catering to specific audiences and including specific characteristics and product IDs.', 'The example demonstrates modifying prompts to focus on specific characteristics desired in the output, resulting in a more tailored description with specific product IDs included.', 'Iterative refinement is crucial for creating successful prompts, as demonstrated by the process of refining a prompt to achieve desired results.', 'Showcasing an example of an iterative process leading to successful prompt generation exemplifies the importance of refining prompts for optimal outcomes.', 'Emphasizing the need for clarity and specificity in prompts highlights the key factors for effective prompt creation.', 'Discussing the importance of allowing the model time to think emphasizes a crucial best practice for prompt generation.', 'Testing a more complex prompt to demonstrate the capabilities of ChatGPT provides insight into the potential of the AI model.']}, {'end': 2510.38, 'segs': [{'end': 2174.021, 'src': 'embed', 'start': 2146.485, 'weight': 1, 'content': [{'end': 2149.006, 'text': 'For more sophisticated applications.', 'start': 2146.485, 'duration': 2.521}, {'end': 2157.531, 'text': 'sometimes you will have multiple examples, say a list of 10 or even 50 or 100 fact sheets,', 'start': 2149.006, 'duration': 8.525}, {'end': 2163.835, 'text': 'and iteratively develop a prompt and evaluate it against a large set of cases.', 'start': 2157.531, 'duration': 6.304}, {'end': 2174.021, 'text': 'But for the early development of most applications I see many people developing it sort of the way I am, with just one example.', 'start': 2166.172, 'duration': 7.849}], 'summary': 'Developing applications with one example, but may need multiple for sophisticated cases.', 'duration': 27.536, 'max_score': 2146.485, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2146485.jpg'}, {'end': 2238.327, 'src': 'embed', 'start': 2211.448, 'weight': 0, 'content': [{'end': 2221.252, 'text': "And when you're done, let's go on to the next video, where we'll talk about one very common use of large language models in software applications,", 'start': 2211.448, 'duration': 9.804}, {'end': 2222.953, 'text': 'which is to summarize text.', 'start': 2221.252, 'duration': 1.701}, {'end': 2225.594, 'text': "So when you're ready, let's go on to the next video.", 'start': 2223.453, 'duration': 2.141}, {'end': 2238.327, 'text': "There's so much text in today's world, pretty much none of us have enough time to read all the things we wish we had time to.", 'start': 2231.584, 'duration': 6.743}], 'summary': 'Large language models used for text summarization in software applications to save time.', 'duration': 26.879, 'max_score': 2211.448, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2211448.jpg'}, {'end': 2329.447, 'src': 'embed', 'start': 2299.979, 'weight': 3, 'content': [{'end': 2309.241, 'text': 'Having a tool to summarize the lengthy reviews could give you a way to very quickly glance over more reviews to get a better sense of what all your customers are thinking.', 'start': 2299.979, 'duration': 9.262}, {'end': 2312.721, 'text': "So here's a prompt for generating a summary.", 'start': 2310.581, 'duration': 2.14}, {'end': 2320.023, 'text': 'Your task is to generate a short summary of a product review from an e-commerce website, summarize the review below, and so on, in at most 30 words.', 'start': 2313.621, 'duration': 6.402}, {'end': 2329.447, 'text': 'And so this is soft and cute, panda plush toy loved by daughter, a bit small for the price, arrived early.', 'start': 2323.124, 'duration': 6.323}], 'summary': 'Panda plush toy: soft, cute, loved by daughter, but a bit small for the price and arrived early.', 'duration': 29.468, 'max_score': 2299.979, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2299979.jpg'}, {'end': 2382.166, 'src': 'embed', 'start': 2342.082, 'weight': 2, 'content': [{'end': 2347.606, 'text': 'Now, sometimes, when creating a summary, if you have a very specific purpose in mind for the summary.', 'start': 2342.082, 'duration': 5.524}, {'end': 2351.848, 'text': 'for example, if you want to give feedback to the shipping department,', 'start': 2347.606, 'duration': 4.242}, {'end': 2361.314, 'text': 'you can also modify the prompt to reflect that so that they can generate a summary that is more applicable to one particular group in your business.', 'start': 2351.848, 'duration': 9.466}, {'end': 2363.476, 'text': 'So, for example,', 'start': 2361.975, 'duration': 1.501}, {'end': 2382.166, 'text': "if I add to give feedback to the shipping department and let's say I change this to start to focus on any aspects that mention shipping and delivery of the product,", 'start': 2363.476, 'duration': 18.69}], 'summary': 'Customize the summary for specific purposes, e.g., giving feedback to the shipping department.', 'duration': 40.084, 'max_score': 2342.082, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2342082.jpg'}], 'start': 2099.859, 'title': 'Prompt development in large language models', 'summary': 'Emphasizes the iterative process of prompt development and discusses the application of large language models in summarizing text for efficient reading. it also explores the use of a tool to generate short summaries of product reviews from an e-commerce website.', 'chapters': [{'end': 2277.999, 'start': 2099.859, 'title': 'Effective prompt development and iterative process', 'summary': 'Emphasizes the iterative process of prompt development, from trying something, evaluating its effectiveness, to iterating and testing against larger sets of examples, and discusses the application of large language models in summarizing text for efficient reading.', 'duration': 178.14, 'highlights': ['The key to being an effective, prompt engineer is having a good process to develop prompts that are effective for your application. Emphasizes the importance of having a good process for prompt development, focusing on effectiveness for the specific application.', 'Iteratively develop a prompt and evaluate it against a large set of cases, where for more mature applications, evaluating prompts against a larger set of examples becomes useful for driving incremental prompt improvement. Highlights the iterative development process and the need to evaluate prompts against larger sets of examples for mature applications to drive incremental improvement.', 'Large language models are being used to summarize text in software applications, enabling efficient reading and content digestion. Discusses the application of large language models in summarizing text for efficient reading and mentions its use in software applications.']}, {'end': 2510.38, 'start': 2279.116, 'title': 'Product review summarization tool', 'summary': 'Explores the use of a tool to generate short summaries of product reviews from an e-commerce website, allowing for customization based on specific business needs, such as shipping or pricing feedback.', 'duration': 231.264, 'highlights': ['A tool to summarize lengthy product reviews for e-commerce websites can provide a quick overview of customer sentiments, aiding in better understanding customer feedback.', 'Customizing the summary prompt allows for generating feedback specific to different business departments, such as shipping or pricing, based on their needs and focus areas.', "The tool can also extract relevant information rather than providing a summary, catering to specific departments' needs, such as summarizing shipping details for the shipping department.", 'By modifying the prompt, the tool can focus on specific aspects like shipping and delivery or price and perceived value, generating summaries tailored to the targeted business department.']}], 'duration': 410.521, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2099859.jpg', 'highlights': ['Large language models summarize text for efficient reading in software applications.', 'Iterative prompt development and evaluation against a large set of cases drive incremental improvement.', 'Customizing summary prompts allows for generating feedback specific to different business departments.', 'A tool to summarize product reviews for e-commerce websites aids in understanding customer feedback.']}, {'end': 2926.718, 'segs': [{'end': 2533.626, 'src': 'embed', 'start': 2511.997, 'weight': 1, 'content': [{'end': 2521.161, 'text': 'Lastly, let me just share with you a concrete example for how to use this in a workflow to help summarize multiple reviews to make them easier to read.', 'start': 2511.997, 'duration': 9.164}, {'end': 2525.702, 'text': 'So here are a few reviews.', 'start': 2523.141, 'duration': 2.561}, {'end': 2529.784, 'text': "This is kind of long, but here's a second review for a standing lamp.", 'start': 2526.263, 'duration': 3.521}, {'end': 2530.945, 'text': 'Need a lamp in the bedroom.', 'start': 2529.844, 'duration': 1.101}, {'end': 2533.626, 'text': "Here's a third review for an electric toothbrush.", 'start': 2531.345, 'duration': 2.281}], 'summary': 'Using nlp to summarize multiple reviews for easier reading.', 'duration': 21.629, 'max_score': 2511.997, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2511997.jpg'}, {'end': 2641.642, 'src': 'embed', 'start': 2618.669, 'weight': 4, 'content': [{'end': 2626.661, 'text': 'to take huge numbers of reviews, generate short summaries of them so that you or someone else can browse the reviews much more quickly.', 'start': 2618.669, 'duration': 7.992}, {'end': 2632.544, 'text': 'And then, if they wish, maybe click in to see the original longer review,', 'start': 2627.062, 'duration': 5.482}, {'end': 2637.627, 'text': 'and this can help you efficiently get a better sense of what all of your customers are thinking.', 'start': 2632.544, 'duration': 5.083}, {'end': 2641.642, 'text': "All right, so that's it for summarizing.", 'start': 2639.821, 'duration': 1.821}], 'summary': 'Summarize huge numbers of reviews for efficient browsing and understanding customer sentiment.', 'duration': 22.973, 'max_score': 2618.669, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2618669.jpg'}, {'end': 2884.536, 'src': 'embed', 'start': 2860.319, 'weight': 0, 'content': [{'end': 2866.643, 'text': 'So, large language models are pretty good at extracting specific things out of a piece of text.', 'start': 2860.319, 'duration': 6.324}, {'end': 2869.385, 'text': "In this case, we're expressing the emotions.", 'start': 2866.663, 'duration': 2.722}, {'end': 2875.89, 'text': 'And this could be useful for understanding how your customers think about a particular product.', 'start': 2870.366, 'duration': 5.524}, {'end': 2884.536, 'text': "For a lot of customer support organizations, it's important to understand if a particular user is extremely upset.", 'start': 2877.271, 'duration': 7.265}], 'summary': 'Language models extract emotions from text for customer insights.', 'duration': 24.217, 'max_score': 2860.319, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2860319.jpg'}], 'start': 2511.997, 'title': 'Efficient text summarization', 'summary': 'Covers efficient text summarization techniques, including workflows to summarize multiple reviews and using prompts for quick extraction of sentiment and emotions from large volumes of text. examples include summarizing reviews for standing lamp, electric toothbrush, and a blender, and providing insights into customer feedback with large language models.', 'chapters': [{'end': 2549.195, 'start': 2511.997, 'title': 'Summarizing reviews', 'summary': 'Discusses using a workflow to summarize multiple reviews, making them easier to read, with examples including reviews for a standing lamp, electric toothbrush, and a blender.', 'duration': 37.198, 'highlights': ['Using a workflow to summarize multiple reviews makes them easier to read', 'Example reviews include standing lamp, electric toothbrush, and blender', 'Concrete example provided for applying the workflow to review summarization']}, {'end': 2926.718, 'start': 2549.615, 'title': 'Summarizing and analyzing text efficiently', 'summary': 'Demonstrates the use of prompts to efficiently summarize and analyze large volumes of text, enabling quick extraction of sentiment and emotions, and providing insights into customer feedback with large language models.', 'duration': 377.103, 'highlights': ['The large language model can efficiently summarize and analyze large volumes of text, enabling quick extraction of sentiment and emotions. The model can be used to summarize hundreds of reviews into short summaries, allowing for efficient browsing of customer feedback.', 'Prompt-based analysis provides quick extraction of sentiment and emotions, reducing the need for supervised learning for classification tasks. The model can analyze the sentiment of product reviews and identify a list of emotions expressed by the writer, providing quick insights without the need for supervised learning.', 'Efficiently extract sentiment with a single prompt, eliminating the need for training and deploying separate models for different tasks. Using a single prompt, the model can classify the sentiment of product reviews, providing a quick and concise response, simplifying post-processing of the sentiment analysis.']}], 'duration': 414.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2511997.jpg', 'highlights': ['The large language model can efficiently summarize and analyze large volumes of text, enabling quick extraction of sentiment and emotions.', 'Using a workflow to summarize multiple reviews makes them easier to read.', 'Prompt-based analysis provides quick extraction of sentiment and emotions, reducing the need for supervised learning for classification tasks.', 'Example reviews include standing lamp, electric toothbrush, and blender.', 'The model can be used to summarize hundreds of reviews into short summaries, allowing for efficient browsing of customer feedback.']}, {'end': 3805.033, 'segs': [{'end': 2959.355, 'src': 'embed', 'start': 2926.878, 'weight': 0, 'content': [{'end': 2937.32, 'text': 'Maybe ask if the customer is expressing delight or ask if there are any missing parts and see if you can get a prompt to make different inferences about this lab review.', 'start': 2926.878, 'duration': 10.442}, {'end': 2951.842, 'text': 'Let me show some more things that you can do with this system, specifically extracting richer information from a customer review.', 'start': 2940, 'duration': 11.842}, {'end': 2959.355, 'text': 'So information extraction is the part of NLP, of natural language processing,', 'start': 2953.271, 'duration': 6.084}], 'summary': 'Nlp includes information extraction for customer reviews.', 'duration': 32.477, 'max_score': 2926.878, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2926878.jpg'}, {'end': 3006.306, 'src': 'embed', 'start': 2983.319, 'weight': 2, 'content': [{'end': 2991.381, 'text': 'it might be useful for your launch collection reviews to figure out what were the items who made the item, figure out positive and negative sentiment,', 'start': 2983.319, 'duration': 8.062}, {'end': 2997.423, 'text': 'to track trends about positive or negative sentiment for specific items or for specific manufacturers.', 'start': 2991.381, 'duration': 6.042}, {'end': 3006.306, 'text': "And in this example, I'm going to ask it to format your response as a JSON object with item and brand as the keys.", 'start': 2998.444, 'duration': 7.862}], 'summary': 'Analyze launch collection reviews to track positive/negative sentiment for items and manufacturers in json format.', 'duration': 22.987, 'max_score': 2983.319, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2983319.jpg'}, {'end': 3053.618, 'src': 'embed', 'start': 3032.975, 'weight': 1, 'content': [{'end': 3042.673, 'text': 'One way to extract all of this information would be to use three or four prompts and call get completion.', 'start': 3032.975, 'duration': 9.698}, {'end': 3044.193, 'text': 'you know, three times or four times.', 'start': 3042.673, 'duration': 1.52}, {'end': 3047.295, 'text': 'extract these different fields, one at a time.', 'start': 3044.193, 'duration': 3.102}, {'end': 3053.618, 'text': 'But it turns out you can actually write a single prompt to extract all of this information at the same time.', 'start': 3047.895, 'duration': 5.723}], 'summary': 'Using a single prompt, all information can be extracted at once.', 'duration': 20.643, 'max_score': 3032.975, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k3032975.jpg'}, {'end': 3143.906, 'src': 'embed', 'start': 3114.164, 'weight': 3, 'content': [{'end': 3119.868, 'text': "Now, one of the cool applications I've seen of large language models is inferring topics.", 'start': 3114.164, 'duration': 5.704}, {'end': 3122.51, 'text': 'Given a long piece of text.', 'start': 3120.529, 'duration': 1.981}, {'end': 3125.553, 'text': 'you know what is this piece of text about?', 'start': 3122.51, 'duration': 3.043}, {'end': 3126.654, 'text': 'What are the topics?', 'start': 3125.773, 'duration': 0.881}, {'end': 3135.1, 'text': "Here's a fictitious newspaper article about how government workers feel about the agency they work for.", 'start': 3127.494, 'duration': 7.606}, {'end': 3143.906, 'text': 'So the recent survey conducted by government, you know, and so on, results reviewed that NASA was a popular department with high satisfaction rating.', 'start': 3135.481, 'duration': 8.425}], 'summary': 'Large language models can infer topics from text, like a survey showing high satisfaction with nasa.', 'duration': 29.742, 'max_score': 3114.164, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k3114164.jpg'}, {'end': 3415.593, 'src': 'embed', 'start': 3389.009, 'weight': 4, 'content': [{'end': 3395.834, 'text': 'How can you take one piece of text and transform it into a different piece of text, such as translate it to a different language?', 'start': 3389.009, 'duration': 6.825}, {'end': 3397.815, 'text': "Let's go on to the next video.", 'start': 3396.334, 'duration': 1.481}, {'end': 3408.526, 'text': 'Large language models are very good at transforming its input to a different format,', 'start': 3403.942, 'duration': 4.584}, {'end': 3415.593, 'text': 'such as inputting a piece of text in one language and transforming it or translating it to a different language,', 'start': 3408.526, 'duration': 7.067}], 'summary': 'Language models can efficiently transform text, e.g., translate it to another language.', 'duration': 26.584, 'max_score': 3389.009, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k3389009.jpg'}, {'end': 3775.568, 'src': 'embed', 'start': 3751.936, 'weight': 5, 'content': [{'end': 3759.84, 'text': 'The way that I would write an email to a colleague or a professor is obviously going to be quite different to the way I text my younger brother.', 'start': 3751.936, 'duration': 7.904}, {'end': 3763.542, 'text': 'And so ChatGPT can actually also help produce different tones.', 'start': 3760.62, 'duration': 2.922}, {'end': 3767.383, 'text': "So let's look at some examples.", 'start': 3765.342, 'duration': 2.041}, {'end': 3774.007, 'text': 'So in this first example, the prompt is translate the following from slang to a business letter.', 'start': 3768.144, 'duration': 5.863}, {'end': 3775.568, 'text': 'Dude, this is Joe.', 'start': 3774.027, 'duration': 1.541}], 'summary': 'Chatgpt can help produce different tones for different communication contexts.', 'duration': 23.632, 'max_score': 3751.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k3751936.jpg'}], 'start': 2926.878, 'title': 'Nlp and large language models', 'summary': 'Demonstrates nlp for extracting specific information from customer reviews and large language models for inferring topics from text, showcasing capabilities for sentiment analysis, trend tracking, translation, and transforming language tones and formats.', 'chapters': [{'end': 3112.15, 'start': 2926.878, 'title': 'Nlp for review analysis', 'summary': 'Demonstrates how to extract specific information such as item purchase and brand from customer reviews using nlp, allowing for sentiment analysis, trend tracking, and json formatting, all from a single prompt.', 'duration': 185.272, 'highlights': ['NLP can extract specific information like item purchase and brand from customer reviews, enabling trend tracking and sentiment analysis. item purchase, brand, sentiment analysis', 'Using a single prompt, NLP can extract multiple fields like sentiment, anger, item purchase, and company from a piece of text. multiple fields extraction from a single prompt', 'The system can format the output as a JSON object with keys for item and brand, which can be easily processed using Python dictionary. JSON formatting for easy processing']}, {'end': 3805.033, 'start': 3114.164, 'title': 'Large language models applications', 'summary': 'Demonstrates how large language models are used to infer topics from text, such as determining topics in a newspaper article, translating text, and transforming language tones and formats, showcasing the capability to translate various languages and transform text tones and formats using large language models.', 'duration': 690.869, 'highlights': ['Large language models are used to infer topics from text by determining topics in a newspaper article, showcasing the capability to extract a list of topics and determine which topics are covered in a news article. The chapter demonstrates how large language models are used to infer topics from text, such as determining topics in a newspaper article and extracting a list of topics covered in a news article.', 'Large language models can translate text from one language to another, including translating English text to Spanish and identifying languages like French, with the ability to handle formal and informal translations. The chapter showcases the capability of large language models to translate text from one language to another, including translating English text to Spanish and identifying languages like French, with the ability to handle formal and informal translations.', 'ChatGPT can help produce different tones by converting slang into a business letter, demonstrating the ability to transform language tones and formats. The chapter illustrates how ChatGPT can help produce different tones by converting slang into a business letter, showcasing the capability to transform language tones and formats.']}], 'duration': 878.155, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k2926878.jpg', 'highlights': ['NLP can extract specific information like item purchase and brand from customer reviews, enabling trend tracking and sentiment analysis.', 'Using a single prompt, NLP can extract multiple fields like sentiment, anger, item purchase, and company from a piece of text.', 'The system can format the output as a JSON object with keys for item and brand, which can be easily processed using Python dictionary.', 'Large language models are used to infer topics from text by determining topics in a newspaper article, showcasing the capability to extract a list of topics and determine which topics are covered in a news article.', 'Large language models can translate text from one language to another, including translating English text to Spanish and identifying languages like French, with the ability to handle formal and informal translations.', 'ChatGPT can help produce different tones by converting slang into a business letter, demonstrating the ability to transform language tones and formats.']}, {'end': 4516.2, 'segs': [{'end': 3925.391, 'src': 'embed', 'start': 3862.206, 'weight': 1, 'content': [{'end': 3870.048, 'text': "So we're going to use this display function from this Python library, display HTML response.", 'start': 3862.206, 'duration': 7.842}, {'end': 3877.67, 'text': 'And here you can see that this is a properly formatted HTML table.', 'start': 3873.369, 'duration': 4.301}, {'end': 3885.235, 'text': "The next transformation task we're going to do is spell check and grammar checking.", 'start': 3880.011, 'duration': 5.224}, {'end': 3889.218, 'text': 'And this is a really kind of popular use for ChatGPT.', 'start': 3885.755, 'duration': 3.463}, {'end': 3891.24, 'text': 'I highly recommend doing this.', 'start': 3889.378, 'duration': 1.862}, {'end': 3892.18, 'text': 'I do this all the time.', 'start': 3891.36, 'duration': 0.82}, {'end': 3895.483, 'text': "And it's especially useful when you're working in a non-native language.", 'start': 3892.801, 'duration': 2.682}, {'end': 3903.149, 'text': 'And so here are some examples of some common grammar and spelling problems and how the language model can help address these.', 'start': 3895.943, 'duration': 7.206}, {'end': 3911.026, 'text': "So I'm going to paste in a list of sentences that have some grammatical or spelling errors.", 'start': 3904.284, 'duration': 6.742}, {'end': 3925.391, 'text': "And then we're going to loop through each of these sentences and ask the model to proofread these.", 'start': 3912.186, 'duration': 13.205}], 'summary': 'Using python library for html display, performing spell and grammar checks with chatgpt.', 'duration': 63.185, 'max_score': 3862.206, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k3862206.jpg'}, {'end': 4073.66, 'src': 'embed', 'start': 4040.769, 'weight': 4, 'content': [{'end': 4042.31, 'text': "And so now we'll do another example.", 'start': 4040.769, 'duration': 1.541}, {'end': 4046.673, 'text': "It's always useful to check your text before you post it in a public forum.", 'start': 4042.63, 'duration': 4.043}, {'end': 4049.295, 'text': "And so we'll go through an example of checking a review.", 'start': 4046.993, 'duration': 2.302}, {'end': 4052.505, 'text': 'And so here is a review about a stuffed panda.', 'start': 4050.444, 'duration': 2.061}, {'end': 4056.428, 'text': "And so we're going to ask the model to proofread and correct the review.", 'start': 4053.026, 'duration': 3.402}, {'end': 4061.552, 'text': 'Great So we have this corrected version.', 'start': 4059.87, 'duration': 1.682}, {'end': 4069.117, 'text': "And one cool thing we can do is find the kind of differences between our original review and the model's output.", 'start': 4062.252, 'duration': 6.865}, {'end': 4073.66, 'text': "So we're going to use this red lines Python package to do this.", 'start': 4069.777, 'duration': 3.883}], 'summary': 'Demonstration of text proofreading using a python package with a corrected review.', 'duration': 32.891, 'max_score': 4040.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k4040769.jpg'}, {'end': 4161.356, 'src': 'embed', 'start': 4109.331, 'weight': 3, 'content': [{'end': 4115.053, 'text': "so in this prompt we're going to ask the model to proofread and correct this same review,", 'start': 4109.331, 'duration': 5.722}, {'end': 4120.774, 'text': 'but also make it more compelling and ensure that it follows APA style and targets an advanced reader,', 'start': 4115.053, 'duration': 5.721}, {'end': 4127.756, 'text': "and we're also going to ask for the output in markdown format, and so we're using the same text from the original review up here.", 'start': 4120.774, 'duration': 6.982}, {'end': 4128.716, 'text': "so let's execute this.", 'start': 4127.756, 'duration': 0.96}, {'end': 4138.202, 'text': 'And here we have a expanded APA style review of the soft panda.', 'start': 4132.198, 'duration': 6.004}, {'end': 4140.903, 'text': 'So this is it for the transforming video.', 'start': 4139.282, 'duration': 1.621}, {'end': 4149.567, 'text': "Next up we have expanding where we'll take a shorter prompt and kind of generate a longer, more freeform response from a language model.", 'start': 4141.263, 'duration': 8.304}, {'end': 4161.356, 'text': 'Expanding is the task of taking a shorter piece of text, such as a set of instructions or a list of topics,', 'start': 4155.231, 'duration': 6.125}], 'summary': 'Model requested to proofread, enhance apa style, and target advanced reader in markdown format.', 'duration': 52.025, 'max_score': 4109.331, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k4109331.jpg'}, {'end': 4260.072, 'src': 'embed', 'start': 4232.441, 'weight': 2, 'content': [{'end': 4235.483, 'text': "And now we're going to write a custom email response to a customer review.", 'start': 4232.441, 'duration': 3.042}, {'end': 4241.529, 'text': "And so given a customer review and the sentiment, we're going to generate a custom response.", 'start': 4236.084, 'duration': 5.445}, {'end': 4252.631, 'text': "Now we're going to use the language model to generate a custom email to a customer based on a customer review and the sentiment of the review.", 'start': 4242.509, 'duration': 10.122}, {'end': 4260.072, 'text': "So we've already extracted the sentiment using the kind of prompts that we saw in the inferring video.", 'start': 4253.171, 'duration': 6.901}], 'summary': 'Generating custom email response based on customer review sentiment.', 'duration': 27.631, 'max_score': 4232.441, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k4232441.jpg'}, {'end': 4317.203, 'src': 'embed', 'start': 4293.456, 'weight': 0, 'content': [{'end': 4300.021, 'text': 'make sure to use specific details from the review, write in a concise and professional tone, and sign the email as AI customer agent.', 'start': 4293.456, 'duration': 6.565}, {'end': 4305.666, 'text': "And when you're using a language model to generate text that you're going to show to a user.", 'start': 4300.502, 'duration': 5.164}, {'end': 4312.051, 'text': "it's very important to have this kind of transparency and let the user know that the text they're seeing was generated by AI.", 'start': 4305.666, 'duration': 6.385}, {'end': 4317.203, 'text': "and then we'll just input the customer review and the review sentiment.", 'start': 4314.141, 'duration': 3.062}], 'summary': 'Use specific details, be transparent about ai-generated text, input customer review and sentiment.', 'duration': 23.747, 'max_score': 4293.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k4293456.jpg'}, {'end': 4362.388, 'src': 'embed', 'start': 4332.523, 'weight': 8, 'content': [{'end': 4335.864, 'text': 'And so here we have a response to the customer.', 'start': 4332.523, 'duration': 3.341}, {'end': 4345.327, 'text': 'It addresses details that the customer mentioned in their review and kind of, as we instructed, suggests that they reach out to customer service,', 'start': 4335.884, 'duration': 9.443}, {'end': 4348.048, 'text': 'because this is just an AI customer service agent.', 'start': 4345.327, 'duration': 2.721}, {'end': 4362.388, 'text': "Next we're going to use a parameter of the language model called temperature that will allow us to change the kind of variety of the model's responses.", 'start': 4351.044, 'duration': 11.344}], 'summary': 'Ai customer service suggests reaching out to customer service due to limitations. exploring language model parameter for varied responses.', 'duration': 29.865, 'max_score': 4332.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k4332523.jpg'}], 'start': 3805.393, 'title': 'Nlp text techniques', 'summary': "Demonstrates chatgpt's json to html transformation, proofreading, and prompt expansion. it also showcases language model's email response generation based on sentiment analysis and temperature settings, emphasizing transparency in ai-generated text.", 'chapters': [{'end': 4039.308, 'start': 3805.393, 'title': 'Text transformation techniques', 'summary': 'Demonstrates the use of chatgpt to transform json to html, proofread and correct grammar and spelling errors, and improve iterative prompt development.', 'duration': 233.915, 'highlights': ['The model is able to correct all of these grammatical errors, making it especially useful when working in a non-native language.', 'The chapter demonstrates the use of ChatGPT to transform JSON to HTML and displays the HTML response using the display function from a Python library.', 'The next transformation task involves spell check and grammar checking, which is a popular use for ChatGPT and highly recommended by the speaker.']}, {'end': 4230.419, 'start': 4040.769, 'title': 'Text proofreading and expansion', 'summary': "Discusses using a language model to proofread and expand text, showcasing an example of correcting a review, finding differences between original and model's output, and expanding a prompt to generate a longer, more freeform response.", 'duration': 189.65, 'highlights': ["The model is used to proofread and correct a review about a stuffed panda, showcasing the capability to make corrections and find differences between the original text and the model's output. review about a stuffed panda", 'The model is prompted to proofread, correct, and make the review more compelling and in APA style, and the output is requested in markdown format, demonstrating the ability to enhance the review and produce it in a specific style. APA style review', "The concept of 'expanding' is introduced, which involves using a language model to generate a longer, more freeform response from a shorter prompt, with potential use cases and responsible usage emphasized. expanding a prompt to generate a longer, more freeform response"]}, {'end': 4516.2, 'start': 4232.441, 'title': 'Custom email response generation', 'summary': 'Discusses generating a custom email response to a customer review based on sentiment analysis and using a language model with varying temperature to achieve different outputs. it emphasizes the importance of transparency in ai-generated text and provides recommendations for choosing temperature settings.', 'duration': 283.759, 'highlights': ['The chapter discusses generating a custom email response to a customer review based on sentiment analysis. The process involves using sentiment analysis to craft a tailored email response to a customer review, showcasing the practical application of sentiment analysis in customer service.', 'The importance of transparency in AI-generated text is emphasized. The chapter highlights the significance of informing users that the text they encounter has been generated by AI, ensuring transparency in AI-powered communication.', 'Recommendations are provided for choosing temperature settings in a language model. The chapter advises using a temperature setting of zero for reliable and predictable responses, and higher temperatures for a wider variety of outputs, catering to different creative applications.']}], 'duration': 710.807, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k3805393.jpg', 'highlights': ['The chapter emphasizes the importance of transparency in AI-generated text.', 'The chapter demonstrates the use of ChatGPT to transform JSON to HTML and displays the HTML response using the display function from a Python library.', 'The chapter discusses generating a custom email response to a customer review based on sentiment analysis.', 'The model is prompted to proofread, correct, and make the review more compelling and in APA style, and the output is requested in markdown format, demonstrating the ability to enhance the review and produce it in a specific style.', "The model is used to proofread and correct a review about a stuffed panda, showcasing the capability to make corrections and find differences between the original text and the model's output.", 'The next transformation task involves spell check and grammar checking, which is a popular use for ChatGPT and highly recommended by the speaker.', "The concept of 'expanding' is introduced, which involves using a language model to generate a longer, more freeform response from a shorter prompt, with potential use cases and responsible usage emphasized.", 'The model is able to correct all of these grammatical errors, making it especially useful when working in a non-native language.', 'Recommendations are provided for choosing temperature settings in a language model.']}, {'end': 5427.943, 'segs': [{'end': 4547.433, 'src': 'embed', 'start': 4516.8, 'weight': 0, 'content': [{'end': 4524.065, 'text': 'Maybe you could pause the video now and try this prompt with a variety of different temperatures just to see how the outputs vary.', 'start': 4516.8, 'duration': 7.265}, {'end': 4531.33, 'text': 'So to summarize, at higher temperatures, the outputs from the model are kind of more random.', 'start': 4525.706, 'duration': 5.624}, {'end': 4537.794, 'text': 'You can almost think of it as that at higher temperatures, the assistant is more distractible, but maybe more creative.', 'start': 4531.61, 'duration': 6.184}, {'end': 4547.433, 'text': "In the next video, we're going to talk more about the chat completions endpoint format and how you can create a custom chatbot using this format.", 'start': 4539.591, 'duration': 7.842}], 'summary': 'Testing the prompt with different temperatures showed more random outputs at higher temperatures. higher temperatures lead to a more distractible but potentially more creative assistant.', 'duration': 30.633, 'max_score': 4516.8, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k4516800.jpg'}, {'end': 4972.133, 'src': 'embed', 'start': 4946.924, 'weight': 1, 'content': [{'end': 4954.766, 'text': 'And the model is able to respond because it has all of the context it needs, um, in this kind of list of messages that we input to it.', 'start': 4946.924, 'duration': 7.842}, {'end': 4958.047, 'text': "So now you're going to build your own chatbot.", 'start': 4956.106, 'duration': 1.941}, {'end': 4968.812, 'text': "This chatbot is going to be called autobot and we're going to automate the collection of user prompts and assistant responses in order to build this order bot.", 'start': 4959.307, 'duration': 9.505}, {'end': 4972.133, 'text': "And it's going to take orders at a pizza restaurant.", 'start': 4969.752, 'duration': 2.381}], 'summary': 'Developing a chatbot named autobot to automate order collection at a pizza restaurant.', 'duration': 25.209, 'max_score': 4946.924, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k4946924.jpg'}, {'end': 5054.606, 'src': 'embed', 'start': 5018.21, 'weight': 4, 'content': [{'end': 5022.211, 'text': "And so here's the context and it contains the system message that contains the menu.", 'start': 5018.21, 'duration': 4.001}, {'end': 5031.464, 'text': "And note that every time we call the language model, we're going to use the same context and the context is building up over time.", 'start': 5023.758, 'duration': 7.706}, {'end': 5037.008, 'text': "And then let's execute this.", 'start': 5035.367, 'duration': 1.641}, {'end': 5047.456, 'text': "Okay, I'm going to say, hi, I would like to order a pizza.", 'start': 5042.952, 'duration': 4.504}, {'end': 5054.606, 'text': 'And the assistant says, great.', 'start': 5053.186, 'duration': 1.42}], 'summary': 'Language model used consistently for context building, enabling seamless conversation flow.', 'duration': 36.396, 'max_score': 5018.21, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k5018210.jpg'}, {'end': 5233.017, 'src': 'embed', 'start': 5206.54, 'weight': 2, 'content': [{'end': 5211.361, 'text': "And we're saying create a JSON summary of the previous food order, itemize the price for each item.", 'start': 5206.54, 'duration': 4.821}, {'end': 5222.905, 'text': 'The fields should be one pizza includes side, two lists of toppings, three lists of drinks, and four lists of sides, and finally the total price.', 'start': 5211.981, 'duration': 10.924}, {'end': 5228.066, 'text': 'And you could also use a user message here.', 'start': 5223.505, 'duration': 4.561}, {'end': 5229.647, 'text': 'This does not have to be a system message.', 'start': 5228.106, 'duration': 1.541}, {'end': 5233.017, 'text': "So let's execute this.", 'start': 5231.877, 'duration': 1.14}], 'summary': 'Create a json summary of the previous food order with itemized prices for each item, including one pizza with side, two lists of toppings, three lists of drinks, four lists of sides, and the total price.', 'duration': 26.477, 'max_score': 5206.54, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k5206540.jpg'}, {'end': 5330.956, 'src': 'embed', 'start': 5307.575, 'weight': 3, 'content': [{'end': 5315.198, 'text': 'And we went through a few capabilities of large language models that are useful for many applications, specifically summarizing, inferring,', 'start': 5307.575, 'duration': 7.623}, {'end': 5317.058, 'text': 'transforming and expanding.', 'start': 5315.198, 'duration': 1.86}, {'end': 5320.58, 'text': 'And you also saw how to build a custom chatbot.', 'start': 5317.719, 'duration': 2.861}, {'end': 5326.882, 'text': 'That was a lot that you learned in just one short course, and I hope you enjoyed going through these materials.', 'start': 5321.2, 'duration': 5.682}, {'end': 5330.956, 'text': "We hope you'll come up with some ideas for applications that you can build yourself now.", 'start': 5327.455, 'duration': 3.501}], 'summary': 'Large language models useful for summarizing, inferring, transforming, and expanding. also learned to build a custom chatbot.', 'duration': 23.381, 'max_score': 5307.575, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k5307575.jpg'}, {'end': 5417.729, 'src': 'embed', 'start': 5385.399, 'weight': 5, 'content': [{'end': 5385.899, 'text': 'Fully agree.', 'start': 5385.399, 'duration': 0.5}, {'end': 5390.442, 'text': 'I think in this age, people that build AI systems can have a huge impact on others.', 'start': 5385.999, 'duration': 4.443}, {'end': 5395.186, 'text': "So it's more important than ever that all of us only use these tools responsibly.", 'start': 5390.502, 'duration': 4.684}, {'end': 5403.559, 'text': 'And I think building large language model based applications is just a very exciting and growing field right now.', 'start': 5396.914, 'duration': 6.645}, {'end': 5411.985, 'text': "And now that you've finished this course, I think you now have a wealth of knowledge to let you build things that few people today know how to.", 'start': 5404.16, 'duration': 7.825}, {'end': 5417.729, 'text': 'So I hope you also help us to spread the word and encourage others to take this course too.', 'start': 5412.626, 'duration': 5.103}], 'summary': 'Building ai systems can have a huge impact. large language model applications are exciting and growing.', 'duration': 32.33, 'max_score': 5385.399, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k5385399.jpg'}], 'start': 4516.8, 'title': 'Building chatbots & model outputs', 'summary': 'Explores the impact of temperature on model outputs, the process of building an autobot chatbot for a pizza restaurant, and principles for creating chatbots and using large language models, providing insights and examples for effective implementation.', 'chapters': [{'end': 4801.239, 'start': 4516.8, 'title': "Temperature's impact on model outputs", 'summary': 'Discusses the impact of temperature on model outputs, showing that at higher temperatures, the outputs become more random and the assistant becomes more distractible but possibly more creative. it also introduces the concept of using a large language model to build a custom chatbot with only a modest amount of effort.', 'duration': 284.439, 'highlights': ['The chapter discusses the impact of temperature on model outputs, showing that at higher temperatures, the outputs become more random and the assistant becomes more distractible but possibly more creative.', 'Introduces the concept of using a large language model to build a custom chatbot with only a modest amount of effort.', 'Describes the components of the OpenAI Chat Completions format and how to use it to build a chatbot.', 'Explains the helper functions for processing messages in the context of building a chatbot with a large language model.', 'Demonstrates using a list of messages to guide the responses of the assistant, including the use of system messages to set the behavior and persona of the assistant.', "Provides an example of using the helper function to get a completion from a list of messages, showing how the assistant's behavior can be guided by the system message."]}, {'end': 5179.077, 'start': 4817.163, 'title': 'Building an autobot chatbot', 'summary': 'Illustrates building a chatbot that collects orders for a pizza restaurant by providing examples of assistant and user messages, explaining the concept of context in language models, and setting up a user interface to interact with the chatbot.', 'duration': 361.914, 'highlights': ['The chapter illustrates building a chatbot that collects orders for a pizza restaurant. The transcript provides examples and explanations for building a chatbot to automate the collection of user prompts and assistant responses for a pizza restaurant.', 'Explaining the concept of context in language models. The concept of context in language models is explained as providing all relevant messages for the model to draw from in the current conversation in order for the model to respond accurately.', 'Setting up a user interface to interact with the chatbot. A user interface is set up to display the autobot, allowing for interaction and conversation with the chatbot to collect orders for the pizza restaurant.']}, {'end': 5427.943, 'start': 5180.658, 'title': 'Building chatbots & large language models', 'summary': 'Covers creating a json summary for a food order, principles for prompting, iterative prompt development, capabilities of large language models, and ethical use of ai.', 'duration': 247.285, 'highlights': ['The chapter covers creating a JSON summary for a food order, itemizing the price for each item, and providing clear and specific instructions for prompt development.', 'Two key principles for prompting are discussed: writing clear and specific instructions and allowing time for thinking.', 'The chapter explores the capabilities of large language models, including summarizing, inferring, transforming, and expanding.', 'Emphasis is placed on using large language models responsibly and building applications that will have a positive impact on others.']}], 'duration': 911.143, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/t_OKMU3E38k/pics/t_OKMU3E38k4516800.jpg', 'highlights': ['The chapter explores the impact of temperature on model outputs, showing that at higher temperatures, the outputs become more random and the assistant becomes more distractible but possibly more creative.', 'The chapter illustrates building a chatbot that collects orders for a pizza restaurant, providing examples and explanations for automating the collection of user prompts and assistant responses.', 'The chapter covers creating a JSON summary for a food order, itemizing the price for each item, and providing clear and specific instructions for prompt development.', 'Introduces the concept of using a large language model to build a custom chatbot with only a modest amount of effort.', 'Explains the concept of context in language models, providing all relevant messages for the model to draw from in the current conversation for accurate responses.', 'Emphasis is placed on using large language models responsibly and building applications that will have a positive impact on others.']}], 'highlights': ['The importance of using API calls to LLMs for quickly building software applications', 'Differentiating between base LLMs and instruction-tuned LLMs', 'Instruction-tuned LLMs are less likely to output problematic texts compared to base LLMs, making them safer and more aligned', 'Practical usage scenarios have been shifting towards the usage of instruction-tuned LLMs, making them more suitable for most practical applications', 'Give clear and specific instructions to the model to avoid assumptions and errors, as demonstrated by providing successful task execution examples', 'The danger of realistic-sounding AI-generated content is highlighted, emphasizing the need to use techniques to avoid it when building applications', 'Iterative prompt development allows for refining the text to cater to different audiences, such as furniture retailers, and including technical details and product IDs', 'Large language models summarize text for efficient reading in software applications', 'The large language model can efficiently summarize and analyze large volumes of text, enabling quick extraction of sentiment and emotions', 'NLP can extract specific information like item purchase and brand from customer reviews, enabling trend tracking and sentiment analysis', 'The chapter emphasizes the importance of transparency in AI-generated text', 'The chapter explores the impact of temperature on model outputs, showing that at higher temperatures, the outputs become more random and the assistant becomes more distractible but possibly more creative', 'The chapter covers creating a JSON summary for a food order, itemizing the price for each item, and providing clear and specific instructions for prompt development', 'Introduces the concept of using a large language model to build a custom chatbot with only a modest amount of effort', 'Emphasis is placed on using large language models responsibly and building applications that will have a positive impact on others']}