title
17. Pair Programming with a Large Language Model | Andrew Ng | DeepLearning.ai - Full Course

description
The course comes from [https://learn.deeplearning.ai/pair-programming-llm/lesson/1/introduction](https://learn.deeplearning.ai/pair-programming-llm/lesson/1/introduction) created by Andrew Ng This short course on pair programming with a large language model (LM) focuses on using Google's Palm API for code generation. In collaboration with Google LMS, the course explores how experienced developers leverage LMs to enhance coding efficiency. The instructor, Lawrence, demonstrates using the Palm API to simplify and improve code, write test cases, debug, and work with complex existing code bases. The course covers necessary setup, including obtaining an API key, installing generative AI libraries, and exploring available models. The tutorial emphasizes experimenting with prompts, utilizing string templates for more powerful prompts, and being cautious about code hallucination. Get free course notes: https://t.me/NoteForYoutubeCourse

detail
{'title': '17. Pair Programming with a Large Language Model | Andrew Ng | DeepLearning.ai - Full Course', 'heatmap': [], 'summary': "Discusses pair programming with a large language model (llm) and google's generative ai libraries, covering topics such as text and code generation, iterating across a list in python, python prompt priming, code optimization, pythonic code, linked list optimization, automated unit testing, llm's bug spotting, and exploring technical debt and ai in swift, providing insights into the practical applications and methodologies for code simplification, productivity improvement, and bug detection using llms and ai tools.", 'chapters': [{'end': 554.088, 'segs': [{'end': 46.27, 'src': 'embed', 'start': 18.241, 'weight': 0, 'content': [{'end': 23.424, 'text': 'And rather than reading the docs, I had an LLM take a first cut at writing the code, which I then fixed.', 'start': 18.241, 'duration': 5.183}, {'end': 28.345, 'text': 'Experienced developers are using LLMs in many ways to speed up our work.', 'start': 24.304, 'duration': 4.041}, {'end': 36.967, 'text': 'In this course, you learn about this set of emerging best practices, including how to get an LLM to help you with error handling,', 'start': 28.925, 'duration': 8.042}, {'end': 38.868, 'text': 'performance improvements and lots more.', 'start': 36.967, 'duration': 1.901}, {'end': 46.27, 'text': "I'm delighted that our instructor for this course is my old friend, Lawrence Moroney, who is lead advocate for AI at Google.", 'start': 39.248, 'duration': 7.022}], 'summary': 'Experienced developers use llms to speed up work. course covers best practices with llm for error handling, performance improvements, and more.', 'duration': 28.029, 'max_score': 18.241, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx018241.jpg'}, {'end': 149.798, 'src': 'embed', 'start': 123.755, 'weight': 1, 'content': [{'end': 129.6, 'text': "In the first lesson in this course, we're going to take a look at how you'll get started with the Palm APIs for code generation.", 'start': 123.755, 'duration': 5.845}, {'end': 135.145, 'text': "Now, of course, in order to be able to do anything, there's some necessary setup, and I'll guide you through that.", 'start': 130.401, 'duration': 4.744}, {'end': 142.431, 'text': 'The Palm APIs and their associated tools are continually being updated on the Google Generative AI site for developers.', 'start': 136.026, 'duration': 6.405}, {'end': 149.798, 'text': 'And this includes Maker Suite, a fast and easy way for you to prototype with generative AI prompting, as well as Vertex AI,', 'start': 143.032, 'duration': 6.766}], 'summary': 'Introduction to palm apis for code generation. updates on maker suite and vertex ai.', 'duration': 26.043, 'max_score': 123.755, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0123755.jpg'}, {'end': 212.05, 'src': 'embed', 'start': 182.514, 'weight': 2, 'content': [{'end': 184.756, 'text': "For the purpose of this course, you don't really need to worry.", 'start': 182.514, 'duration': 2.242}, {'end': 187.979, 'text': "We've made one for you, but it's something that you're going to need to keep in mind.", 'start': 184.816, 'duration': 3.163}, {'end': 191.592, 'text': "Next, you're going to need the generative AI libraries from Google.", 'start': 188.829, 'duration': 2.763}, {'end': 197.097, 'text': "And at the time of filming, they're available in Node.js, Swift, and Python, as well as with a curl interface.", 'start': 191.712, 'duration': 5.385}, {'end': 201.24, 'text': "But for this course, I'll be using Python, and I'll show you how to do a pip install.", 'start': 197.617, 'duration': 3.623}, {'end': 205.645, 'text': 'So of course, it goes without saying that you may need some Python skills.', 'start': 202.281, 'duration': 3.364}, {'end': 209.768, 'text': "If you don't have them, you can check out learnpython.org if you're a bit lost.", 'start': 205.745, 'duration': 4.023}, {'end': 212.05, 'text': "But most of what I'm doing is quite basic.", 'start': 210.129, 'duration': 1.921}], 'summary': 'Course requires generative ai libraries from google, available in node.js, swift, and python, with python skills needed.', 'duration': 29.536, 'max_score': 182.514, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0182514.jpg'}, {'end': 445.489, 'src': 'embed', 'start': 419.214, 'weight': 3, 'content': [{'end': 423.537, 'text': "So again, this is just some ways that you can start using the Palm API to understand what's going on.", 'start': 419.214, 'duration': 4.323}, {'end': 427.219, 'text': 'As the API grows and as the supported models grow,', 'start': 424.457, 'duration': 2.762}, {'end': 431.702, 'text': 'you might be getting different results here and you might be able to try a different model and have a little bit more fun with it.', 'start': 427.219, 'duration': 4.483}, {'end': 434.846, 'text': 'As we see, we have three models end up getting listed out.', 'start': 432.185, 'duration': 2.661}, {'end': 440.328, 'text': "There's the chat bison 001, the text bison 001, and the embedding gecko 001.", 'start': 434.886, 'duration': 5.442}, {'end': 443.369, 'text': 'So you imagine gecko is going to be smaller.', 'start': 440.328, 'duration': 3.041}, {'end': 445.489, 'text': "I mentioned it's based on animal sizes.", 'start': 443.429, 'duration': 2.06}], 'summary': 'The palm api offers three models for use, including chat bison 001, text bison 001, and embedding gecko 001, with potential for future expansion.', 'duration': 26.275, 'max_score': 419.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0419214.jpg'}, {'end': 481.543, 'src': 'embed', 'start': 456.113, 'weight': 4, 'content': [{'end': 464.296, 'text': "So the goal behind a chat bison is that that's more optimized for chat scenarios, where it keeps track of the context.", 'start': 456.113, 'duration': 8.183}, {'end': 469.878, 'text': "So you'll ask it something, it'll give an answer, you might follow up, it'll give another answer, and you might follow up again.", 'start': 464.836, 'duration': 5.042}, {'end': 473.44, 'text': 'Whereas the text bison one is more optimized for single shot.', 'start': 470.359, 'duration': 3.081}, {'end': 476.681, 'text': "You're going to give it a prompt, you're going to get an answer, and then you're going to move on.", 'start': 473.78, 'duration': 2.901}, {'end': 481.543, 'text': "We're going to be using that one today because I find that one generally works much better for code.", 'start': 477.021, 'duration': 4.522}], 'summary': 'Chat bison optimized for context, text bison for single shot, used for coding.', 'duration': 25.43, 'max_score': 456.113, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0456113.jpg'}, {'end': 537.674, 'src': 'embed', 'start': 510.55, 'weight': 5, 'content': [{'end': 513.432, 'text': "so that we don't have to keep writing the same code again and again and again.", 'start': 510.55, 'duration': 2.882}, {'end': 516.114, 'text': "It's always good practice not to repeat yourself.", 'start': 513.953, 'duration': 2.161}, {'end': 518.796, 'text': "So it's always good practice not to repeat yourself.", 'start': 516.254, 'duration': 2.542}, {'end': 520.657, 'text': "It's always good practice not to repeat yourself.", 'start': 518.895, 'duration': 1.762}, {'end': 528.563, 'text': "So I'm going to start this with from google.api core import retry.", 'start': 521.357, 'duration': 7.206}, {'end': 531.088, 'text': 'And what the retry library is.', 'start': 529.346, 'duration': 1.742}, {'end': 537.674, 'text': "it's just something that when you're doing a backend call to something like we're doing here to an LLM database,", 'start': 531.088, 'duration': 6.586}], 'summary': 'Avoid repeating code and import google.api core for backend retries.', 'duration': 27.124, 'max_score': 510.55, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0510550.jpg'}], 'start': 1.072, 'title': "Pair programming with llm and google's generative ai libraries", 'summary': "Introduces pair programming with a large language model (llm) and its partnership with google, highlighting the use of llms to simplify code, improve productivity, and the necessary setup for using the palm api. it also explores google's generative ai libraries, showcasing the process of obtaining an api key, configuring the palm api, and exploring available models, highlighting the difference between chat bison and text bison models and their use cases.", 'chapters': [{'end': 217.735, 'start': 1.072, 'title': 'Pair programming with llm', 'summary': 'Introduces pair programming with a large language model (llm) and its partnership with google, highlighting the use of llms to simplify code, improve productivity, and the necessary setup for using the palm api.', 'duration': 216.663, 'highlights': ['Experienced developers are using LLMs in many ways to speed up work, including error handling, performance improvements, and more. Experienced developers are utilizing LLMs for various tasks such as error handling and performance improvements to speed up their work.', 'The course focuses on using the Palm API to simplify and improve code, write test cases, debug, and refactor code, as well as work with complex existing code bases. The course emphasizes using the Palm API to simplify and improve code, write test cases, debug, and refactor code, particularly in complex existing code bases.', 'Necessary setup includes obtaining an API key and generative AI libraries from Google, available in Node.js, Swift, and Python. The necessary setup involves obtaining an API key and generative AI libraries from Google, available in various languages such as Node.js, Swift, and Python.']}, {'end': 554.088, 'start': 218.496, 'title': "Exploring google's generative ai libraries", 'summary': "Introduces google's generative ai libraries, showcases the process of obtaining an api key, configuring the palm api, and exploring available models, highlighting the difference between chat bison and text bison models and their use cases.", 'duration': 335.592, 'highlights': ['The chapter introduces the process of obtaining an API key, configuring the Palm API, and exploring available models.', 'The difference between chat bison and text bison models is explained, with chat bison optimized for chat scenarios and text bison optimized for single-shot prompts.', 'The process of creating a helper function to generate texts without repeating code is demonstrated using the retry library.']}], 'duration': 553.016, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx01072.jpg', 'highlights': ['Experienced developers are using LLMs in many ways to speed up work, including error handling, performance improvements, and more.', 'The course focuses on using the Palm API to simplify and improve code, write test cases, debug, and refactor code, as well as work with complex existing code bases.', 'The necessary setup includes obtaining an API key and generative AI libraries from Google, available in Node.js, Swift, and Python.', 'The chapter introduces the process of obtaining an API key, configuring the Palm API, and exploring available models.', 'The difference between chat bison and text bison models is explained, with chat bison optimized for chat scenarios and text bison optimized for single-shot prompts.', 'The process of creating a helper function to generate texts without repeating code is demonstrated using the retry library.']}, {'end': 787.433, 'segs': [{'end': 596.504, 'src': 'embed', 'start': 555.049, 'weight': 0, 'content': [{'end': 556.41, 'text': 'And then we can write our function.', 'start': 555.049, 'duration': 1.361}, {'end': 560.715, 'text': 'So our function is going to be called surprisingly generate text.', 'start': 556.731, 'duration': 3.984}, {'end': 563.998, 'text': "And what I'm going to do with generate text is I'm going to pass it my prompt.", 'start': 561.395, 'duration': 2.603}, {'end': 567.647, 'text': "And I'm going to pass it the model that I want to use.", 'start': 565.486, 'duration': 2.161}, {'end': 570.409, 'text': "And we've already created something that we call Model Bison.", 'start': 567.747, 'duration': 2.662}, {'end': 572.27, 'text': "And then I'm going to use a temperature.", 'start': 570.789, 'duration': 1.481}, {'end': 577.834, 'text': 'Now, the default temperature for the model is 0.7.', 'start': 572.771, 'duration': 5.063}, {'end': 581.696, 'text': "But with this temperature of 0.0, it's going to be a much more deterministic model.", 'start': 577.834, 'duration': 3.862}, {'end': 586.459, 'text': "So whatever results I'm getting from prompts here, you should be seeing the same ones.", 'start': 581.776, 'duration': 4.683}, {'end': 590.462, 'text': "Of course, if you're using a later version and you have a later model, it might have changed differently.", 'start': 586.9, 'duration': 3.562}, {'end': 596.504, 'text': "And once we've done that now, we just want to return what palm is going to give us.", 'start': 591.32, 'duration': 5.184}], 'summary': "Function 'generate text' utilizes prompt and model to produce deterministic results with temperature set at 0.0.", 'duration': 41.455, 'max_score': 555.049, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0555049.jpg'}, {'end': 687.04, 'src': 'embed', 'start': 657.602, 'weight': 2, 'content': [{'end': 663.588, 'text': 'This contains the basic command that we want to send to the LLM to instruct it to generate some output for us.', 'start': 657.602, 'duration': 5.986}, {'end': 671.089, 'text': "We'll then use that prompt with the generate text function that we just created and with the model that we selected to get a completion.", 'start': 664.485, 'duration': 6.604}, {'end': 679.015, 'text': "It's worth noting here that generally when using LLMs, the output text from the model is what it predicted the next set of tokens would be.", 'start': 671.75, 'duration': 7.265}, {'end': 687.04, 'text': 'So when you pass in a question, the next set of tokens would usually be an answer and thus it completes the string that you began with the quote.', 'start': 679.615, 'duration': 7.425}], 'summary': 'Using llm to generate output by passing a prompt and model, completing strings based on predicted tokens.', 'duration': 29.438, 'max_score': 657.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0657602.jpg'}, {'end': 731.586, 'src': 'embed', 'start': 699.968, 'weight': 6, 'content': [{'end': 705.992, 'text': "In this course we'll just be printing it out in the Jupyter Notebook, but in a real app you might be injecting code into an IDE,", 'start': 699.968, 'duration': 6.024}, {'end': 708.093, 'text': 'saving it to a repo or many other things.', 'start': 705.992, 'duration': 2.101}, {'end': 713.136, 'text': "So let's explore the code for these steps to do a basic code generation with Palm.", 'start': 708.853, 'duration': 4.283}, {'end': 717.95, 'text': "So now that we've done this, let's just do some very, very basic code generation.", 'start': 714.386, 'duration': 3.564}, {'end': 720.974, 'text': "So I'm going to just give it a very simple prompt.", 'start': 718.811, 'duration': 2.163}, {'end': 731.586, 'text': 'Prompt equals, how about show me how to iterate across a list in Python.', 'start': 721.094, 'duration': 10.492}], 'summary': 'The course covers basic code generation with palm, using a prompt to iterate across a list in python.', 'duration': 31.618, 'max_score': 699.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0699968.jpg'}, {'end': 787.433, 'src': 'embed', 'start': 747.597, 'weight': 7, 'content': [{'end': 756.882, 'text': "So if I say completion equals generate text and pass it that prompt, what do you think is going to happen? Well, let's see.", 'start': 747.597, 'duration': 9.285}, {'end': 758.496, 'text': "I'm going to run it.", 'start': 757.875, 'duration': 0.621}, {'end': 762.782, 'text': "It's going to take a little bit because it's instantiating the APIs.", 'start': 759.157, 'duration': 3.625}, {'end': 764.685, 'text': "It's making the call to the back end.", 'start': 763.202, 'duration': 1.483}, {'end': 767.048, 'text': 'The back end is generating the stuff for us.', 'start': 765.105, 'duration': 1.943}, {'end': 769.331, 'text': "And it's all done.", 'start': 767.488, 'duration': 1.843}, {'end': 773.837, 'text': "If you're not watching the recording and you're doing this in your own notebook, you'll see a little star.", 'start': 770.332, 'duration': 3.505}, {'end': 778.763, 'text': 'in the notebook beside it and when that star turns into the number of the cell,', 'start': 774.378, 'duration': 4.385}, {'end': 787.433, 'text': "then you'll be able to continue and then you'll be able to do something like print completion, dot results and drum roll boom.", 'start': 778.763, 'duration': 8.67}], 'summary': "Running 'completion equals generate text' will instantiate apis, make backend call, and display results.", 'duration': 39.836, 'max_score': 747.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0747597.jpg'}], 'start': 555.049, 'title': 'Text and code generation', 'summary': "Covers the creation of a 'generate text' function with temperature control and code generation using palm api, including process, output text prediction, and basic generation using prompts.", 'chapters': [{'end': 596.504, 'start': 555.049, 'title': 'Text generation function with temperature control', 'summary': "Explains how to create a function called 'generate text' that takes a prompt and a model as input, uses a temperature setting to control the randomness of the output, and returns the generated text, with the default temperature being 0.7 and a deterministic model achieved with a temperature of 0.0.", 'duration': 41.455, 'highlights': ["The function 'generate text' is created to take a prompt and model as input, with the option to adjust the temperature setting for controlling randomness, with the default temperature being 0.7.", 'Using a temperature of 0.0 creates a deterministic model, ensuring consistent results for the given prompts, although variations may occur with different model versions.', 'The function returns the generated text based on the input prompt and model, providing a practical utility for text generation tasks.']}, {'end': 699.348, 'start': 596.544, 'title': 'Code generation using palm api', 'summary': 'Discusses the creation of a generate text function, the process of using prompting to generate code, and the output text prediction from the model when using llms.', 'duration': 102.804, 'highlights': ['The process of creating a generate text function involves defining prompt, model, and temperature parameters to avoid reinventing the wheel.', 'Using prompting with the generate text function allows for code generation, following a pattern of creating a prompt and using it with the selected model to get a completion.', 'When using LLMs, the output text from the model predicts the next set of tokens, such as completing a string with an answer based on the input prompt.', 'After the completion, the output can be handled based on individual preferences.']}, {'end': 787.433, 'start': 699.968, 'title': 'Code generation with palm', 'summary': 'Explores basic code generation using palm, where a prompt is used to generate text, and the completion results are printed.', 'duration': 87.465, 'highlights': ["A prompt is used to initiate basic code generation with Palm, with an example prompt being 'show me how to iterate across a list in Python'.", "The completion results are printed using 'print completion.dot results'.", 'The process of code generation involves instantiating APIs, making a call to the backend, and generating the required content.']}], 'duration': 232.384, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0555049.jpg', 'highlights': ["The function 'generate text' is created to take a prompt and model as input, with the option to adjust the temperature setting for controlling randomness, with the default temperature being 0.7.", 'Using a temperature of 0.0 creates a deterministic model, ensuring consistent results for the given prompts, although variations may occur with different model versions.', 'The function returns the generated text based on the input prompt and model, providing a practical utility for text generation tasks.', 'The process of creating a generate text function involves defining prompt, model, and temperature parameters to avoid reinventing the wheel.', 'Using prompting with the generate text function allows for code generation, following a pattern of creating a prompt and using it with the selected model to get a completion.', 'When using LLMs, the output text from the model predicts the next set of tokens, such as completing a string with an answer based on the input prompt.', "A prompt is used to initiate basic code generation with Palm, with an example prompt being 'show me how to iterate across a list in Python'.", "The completion results are printed using 'print completion.dot results'.", 'The process of code generation involves instantiating APIs, making a call to the backend, and generating the required content.']}, {'end': 1127.501, 'segs': [{'end': 841.189, 'src': 'embed', 'start': 787.433, 'weight': 0, 'content': [{'end': 791.697, 'text': "we're going to see results like this one, and it's giving us to iterate across a list.", 'start': 787.433, 'duration': 4.264}, {'end': 794.738, 'text': 'in Python, you could use the for loop and the syntax would look like this', 'start': 791.697, 'duration': 3.041}, {'end': 802.261, 'text': "And here's like if you have a list with ABC, your for loop for item in my list print item will give you the output ABC.", 'start': 795.638, 'duration': 6.623}, {'end': 807.403, 'text': 'But one of the really useful things about this as well is that it also finds other ways of doing it.', 'start': 803.041, 'duration': 4.362}, {'end': 809.404, 'text': "So for example, there's the enumerate function.", 'start': 807.503, 'duration': 1.901}, {'end': 812.345, 'text': 'And you can see here the enumerate function is working on the list.', 'start': 809.904, 'duration': 2.441}, {'end': 813.785, 'text': 'Sorry about the cropping on the screen.', 'start': 812.365, 'duration': 1.42}, {'end': 817.807, 'text': "But as you look through the code, you'll see how all this kind of stuff is going to work for you.", 'start': 814.206, 'duration': 3.601}, {'end': 821.508, 'text': 'So again, this is very, very simple, very basic code generation.', 'start': 818.327, 'duration': 3.181}, {'end': 828.231, 'text': "But we're going beyond just creating the code because I asked for it to show me how to iterate across a list in Python.", 'start': 821.548, 'duration': 6.683}, {'end': 830.792, 'text': "So it's giving me the code as well as explaining.", 'start': 828.611, 'duration': 2.181}, {'end': 832.693, 'text': "It's showing me, not just giving me code.", 'start': 830.832, 'duration': 1.861}, {'end': 833.933, 'text': 'And this is the first part.', 'start': 833.013, 'duration': 0.92}, {'end': 836.194, 'text': 'And this is really, really basic code generation.', 'start': 833.973, 'duration': 2.221}, {'end': 841.189, 'text': 'Now, as I showed, I asked it to show me how to iterate across a list in Python.', 'start': 837.025, 'duration': 4.164}], 'summary': 'Demonstrates iterating through a list in python using for loop and enumerate function.', 'duration': 53.756, 'max_score': 787.433, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0787433.jpg'}, {'end': 918.275, 'src': 'embed', 'start': 889.996, 'weight': 4, 'content': [{'end': 894.759, 'text': 'is your large language model is going to take what you ask it very, very literally.', 'start': 889.996, 'duration': 4.763}, {'end': 897.24, 'text': "You know, so if you ask it to write code, it'll write code.", 'start': 895.199, 'duration': 2.041}, {'end': 904.123, 'text': 'If you ask it to show you how to do something, you might get some more valuable stuff, as we did here,', 'start': 897.92, 'duration': 6.203}, {'end': 908.045, 'text': 'where it gave me various options to do it and explain them to me as well.', 'start': 904.123, 'duration': 3.922}, {'end': 913.007, 'text': 'So for a little bit of fun, we can also take some of the code that it output, like, for example, here.', 'start': 908.765, 'duration': 4.242}, {'end': 918.275, 'text': 'And this was Python giving me a list ABC and just iterating through that.', 'start': 913.894, 'duration': 4.381}], 'summary': 'A large language model can provide valuable code and explanations when asked, such as python giving a list and iterating through it.', 'duration': 28.279, 'max_score': 889.996, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0889996.jpg'}, {'end': 978.296, 'src': 'embed', 'start': 950.069, 'weight': 1, 'content': [{'end': 956.753, 'text': 'But one of the things that we will be discussing a lot during the course is that output code is prone to hallucination.', 'start': 950.069, 'duration': 6.684}, {'end': 963.196, 'text': "So you really, really should be testing your code thoroughly before you use it in any way that's serious or in production or anything like that.", 'start': 957.173, 'duration': 6.023}, {'end': 964.711, 'text': "So now it's your turn.", 'start': 963.891, 'duration': 0.82}, {'end': 966.192, 'text': 'Maybe you could try something.', 'start': 965.031, 'duration': 1.161}, {'end': 969.333, 'text': 'Prompt equals show me how to.', 'start': 966.992, 'duration': 2.341}, {'end': 971.394, 'text': "And what is it that you'd be excited to see?", 'start': 969.693, 'duration': 1.701}, {'end': 978.296, 'text': 'Could it be some common computer science problems like sorting or counting elements or anything like that?', 'start': 971.894, 'duration': 6.402}], 'summary': 'Output code prone to hallucination, test thoroughly before use.', 'duration': 28.227, 'max_score': 950.069, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0950069.jpg'}, {'end': 1053.07, 'src': 'embed', 'start': 1022.989, 'weight': 3, 'content': [{'end': 1025.531, 'text': "And we're going to be using something called a string template to do that.", 'start': 1022.989, 'duration': 2.542}, {'end': 1035.797, 'text': 'One method for interacting with an LLM that I find really useful is to prime it for a particular type of behavior using your prompt.', 'start': 1028.712, 'duration': 7.085}, {'end': 1037.498, 'text': 'Now what this means?', 'start': 1036.698, 'duration': 0.8}, {'end': 1044.163, 'text': 'that instead of the prompt just saying something like generate code that does whatever, you can also have the prompt look more,', 'start': 1037.498, 'duration': 6.665}, {'end': 1048.266, 'text': 'like you are an expert in clear, well engineered code in Python.', 'start': 1044.163, 'duration': 4.103}, {'end': 1053.07, 'text': 'Please generate code that does whatever and then output it with line by line comments.', 'start': 1048.687, 'duration': 4.383}], 'summary': 'Using string templates to prime llm for specific behavior and code generation.', 'duration': 30.081, 'max_score': 1022.989, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx01022989.jpg'}], 'start': 787.433, 'title': 'Iterating across a list in python and output code testing', 'summary': 'Covers iterating across a list in python using for loop and enumerate function, emphasizing clear prompts for comprehensive results. it also discusses output code testing, stressing thorough testing before production use, and the use of template strings to prime an llm for specific behavior, leading to better performance.', 'chapters': [{'end': 908.045, 'start': 787.433, 'title': 'Iterating across a list in python', 'summary': 'Discusses iterating across a list in python, showcasing the for loop and enumerate function, while emphasizing the value of clear prompts for comprehensive results.', 'duration': 120.612, 'highlights': ["The chapter demonstrates the for loop syntax in Python, illustrating how it can iterate across a list, providing the output 'ABC'.", 'It also introduces the enumerate function, showcasing its functionality in working on a list, and emphasizes the importance of clear prompts for comprehensive results.', "The importance of clear prompts is highlighted, as the large language model is shown to provide more valuable and diverse outputs when asked to 'show' how to do something, compared to simply 'writing' code."]}, {'end': 1127.501, 'start': 908.765, 'title': 'Output code testing and template string', 'summary': 'Covers the process of testing output code, emphasizing the importance of thorough testing before using it in production, along with the use of template strings to prime an llm for a specific behavior, leading to better performance. it also introduces the concept of using string templating to make prompts more powerful and efficient.', 'duration': 218.736, 'highlights': ['The importance of thoroughly testing output code before using it in production is emphasized, ensuring that it works as intended and does not lead to errors or incorrect results. Thorough testing of code before production use', 'The concept of using template strings to prime an LLM for a specific behavior is introduced, improving the performance of the prompt and leading to better results. Introduction of using string templating to prime an LLM for a specific behavior', 'The process of testing output code is demonstrated through an example of iterating through a list in Python and ensuring that the code works as expected, emphasizing the importance of testing code thoroughly. Demonstration of testing output code through Python example']}], 'duration': 340.068, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx0787433.jpg', 'highlights': ["The chapter demonstrates the for loop syntax in Python, illustrating how it can iterate across a list, providing the output 'ABC'.", 'The importance of thoroughly testing output code before using it in production is emphasized, ensuring that it works as intended and does not lead to errors or incorrect results.', 'It also introduces the enumerate function, showcasing its functionality in working on a list, and emphasizes the importance of clear prompts for comprehensive results.', 'The concept of using template strings to prime an LLM for a specific behavior is introduced, improving the performance of the prompt and leading to better results.', "The importance of clear prompts is highlighted, as the large language model is shown to provide more valuable and diverse outputs when asked to 'show' how to do something, compared to simply 'writing' code.", 'The process of testing output code is demonstrated through an example of iterating through a list in Python and ensuring that the code works as expected, emphasizing the importance of testing code thoroughly.']}, {'end': 1818.396, 'segs': [{'end': 1155.323, 'src': 'embed', 'start': 1128.021, 'weight': 1, 'content': [{'end': 1133.986, 'text': 'So the goal here is I always like to think about my prompt as being really made up of three different things.', 'start': 1128.021, 'duration': 5.965}, {'end': 1138.39, 'text': "The first thing that's going to be in the prompt is what I call the priming of the prompt.", 'start': 1134.587, 'duration': 3.803}, {'end': 1141.773, 'text': "It's a little bit like when you prime a wall, when you're painting it right?", 'start': 1138.45, 'duration': 3.323}, {'end': 1145.717, 'text': "It's, you're getting your prompt ready, you're designing it for what the thing is going to do.", 'start': 1141.853, 'duration': 3.864}, {'end': 1148.318, 'text': 'The second part of the prompt is the question.', 'start': 1146.337, 'duration': 1.981}, {'end': 1150.38, 'text': "It's typically what we call the prompt.", 'start': 1148.839, 'duration': 1.541}, {'end': 1155.323, 'text': "It's what we were doing earlier on is like, give me a linked list in Python or show me how to do something.", 'start': 1150.42, 'duration': 4.903}], 'summary': 'The prompt consists of priming, a preparatory phase, and the question itself.', 'duration': 27.302, 'max_score': 1128.021, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx01128021.jpg'}, {'end': 1353.149, 'src': 'embed', 'start': 1325.318, 'weight': 0, 'content': [{'end': 1327.818, 'text': "But let's think about something that's more appropriate for coding.", 'start': 1325.318, 'duration': 2.5}, {'end': 1341.441, 'text': 'So instead of saying work through it step by step and show your work one step per line, how about we do something like insert comments for each Line.', 'start': 1328.358, 'duration': 13.083}, {'end': 1351.968, 'text': "Of code, so if I change my decorator to be that now, which is something it's a little bit more appropriate for coding, I could recreate my prompt.", 'start': 1343.443, 'duration': 8.525}, {'end': 1353.149, 'text': 'I print my prompt.', 'start': 1352.128, 'duration': 1.021}], 'summary': 'Suggests using comments for each line of code to make it more appropriate for coding.', 'duration': 27.831, 'max_score': 1325.318, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx01325318.jpg'}, {'end': 1455.623, 'src': 'embed', 'start': 1423.454, 'weight': 2, 'content': [{'end': 1435.725, 'text': 'So create a very large list of random numbers in Python and then write code to sort that list.', 'start': 1423.454, 'duration': 12.271}, {'end': 1438.047, 'text': 'Classic computer science problem.', 'start': 1436.446, 'duration': 1.601}, {'end': 1440.99, 'text': "This one is completely unrehearsed, so hopefully it's going to work.", 'start': 1438.067, 'duration': 2.923}, {'end': 1442.732, 'text': "So that's my new question.", 'start': 1441.551, 'duration': 1.181}, {'end': 1447.256, 'text': "So I'm going to keep the same priming text and decorator.", 'start': 1443.532, 'duration': 3.724}, {'end': 1450.078, 'text': 'So all I need to do now is create a new prompt.', 'start': 1447.736, 'duration': 2.342}, {'end': 1455.623, 'text': 'So let me create that new prompt with command C.', 'start': 1450.879, 'duration': 4.744}], 'summary': 'Create a large list of random numbers in python and sort it. unrehearsed.', 'duration': 32.169, 'max_score': 1423.454, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx01423454.jpg'}], 'start': 1128.021, 'title': 'Python prompt priming and large language models', 'summary': 'Explores prompt priming, formatting in python, and utilizing large language models for coding assistance, with examples and importance of testing due to potential hallucination.', 'chapters': [{'end': 1284.879, 'start': 1128.021, 'title': 'Prompt priming and formatting in python', 'summary': 'Explains the components of a prompt, including priming, the question, and the decorator, and demonstrates how to format a prompt in python, with examples and explanation.', 'duration': 156.858, 'highlights': ['The prompt is composed of three parts: priming, question, and decorator, each serving a specific function in preparing and structuring the prompt.', 'Demonstrates the process of string formatting in Python to set up a prompt using the priming text, question, and decorator.', 'Provides an example of a formatted prompt, including priming text, a question, and a decorator, demonstrating the output and potential adjustments.']}, {'end': 1818.396, 'start': 1285.039, 'title': 'Using large language models for coding assistance', 'summary': 'Discusses using large language models (llm) for coding assistance, demonstrating the process of generating code using prompts and decorators, exploring a scenario of creating a large list of random numbers in python and sorting it, and highlighting the importance of testing generated code thoroughly due to potential hallucination.', 'duration': 533.357, 'highlights': ['The process of generating code using prompts and decorators is demonstrated, showing how changing the prompt to be more specific and appropriate for coding can yield better results, as seen in the example of creating a doubly linked list and inserting comments for each line of code.', 'An unrehearsed scenario of creating a large list of random numbers in Python and writing code to sort it is explored, highlighting the success of the generated code in both creating the list and sorting it, with the importance of testing thoroughly emphasized.', 'The importance of testing thoroughly before putting generated code into serious use is emphasized, stating that the generated code is prone to hallucination and may vary in output, urging users to test everything thoroughly before implementation.']}], 'duration': 690.375, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx01128021.jpg', 'highlights': ['The process of generating code using prompts and decorators is demonstrated, showing how changing the prompt to be more specific and appropriate for coding can yield better results, as seen in the example of creating a doubly linked list and inserting comments for each line of code.', 'The prompt is composed of three parts: priming, question, and decorator, each serving a specific function in preparing and structuring the prompt.', 'An unrehearsed scenario of creating a large list of random numbers in Python and writing code to sort it is explored, highlighting the success of the generated code in both creating the list and sorting it, with the importance of testing thoroughly emphasized.']}, {'end': 2099.02, 'segs': [{'end': 1838.802, 'src': 'embed', 'start': 1818.396, 'weight': 0, 'content': [{'end': 1829.038, 'text': "the last of the three things that you need to do to get up and running is to set up the generateText helper function that we were talking about earlier on and that we've been using all along.", 'start': 1818.396, 'duration': 10.642}, {'end': 1832.12, 'text': "And in this case, we're going to import the retry.", 'start': 1829.659, 'duration': 2.461}, {'end': 1838.802, 'text': "And then this retry will, when it's doing a backend, call to a backend service, as we're doing with Palm.", 'start': 1832.68, 'duration': 6.122}], 'summary': 'Set up the generatetext helper function and import the retry for backend calls.', 'duration': 20.406, 'max_score': 1818.396, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx01818396.jpg'}, {'end': 2038.205, 'src': 'embed', 'start': 1968.092, 'weight': 1, 'content': [{'end': 1973.957, 'text': 'Instead of iterating for i in range len array print array i, we can start using the star operator.', 'start': 1968.092, 'duration': 5.865}, {'end': 1976.098, 'text': 'And then it will come back and it will tell me.', 'start': 1974.457, 'duration': 1.641}, {'end': 1981.502, 'text': 'I improved the code by using the star operator to unpack the array into individual arguments that print function.', 'start': 1976.098, 'duration': 5.404}, {'end': 1985.025, 'text': 'And this is much more concise and efficient than using a for loop.', 'start': 1981.923, 'duration': 3.102}, {'end': 1986.826, 'text': "I've learned something new today.", 'start': 1985.545, 'duration': 1.281}, {'end': 1987.627, 'text': 'I hope you did too.', 'start': 1986.966, 'duration': 0.661}, {'end': 1989.328, 'text': 'So this was nice.', 'start': 1988.567, 'duration': 0.761}, {'end': 1993.511, 'text': 'This gave us one way of doing it, a more Pythonic way of doing it.', 'start': 1989.388, 'duration': 4.123}, {'end': 1998.154, 'text': 'If you had come from learning Basic or C, or Pascal or Java or something like that,', 'start': 1993.591, 'duration': 4.563}, {'end': 2001.357, 'text': "you're probably more used to the type of loop that I was using in the question.", 'start': 1998.154, 'duration': 3.203}, {'end': 2007.281, 'text': 'But can we take this a little bit further with a new prompt? There may be multiple ways of doing this.', 'start': 2002.317, 'duration': 4.964}, {'end': 2010.063, 'text': "So let's try and see if there's another way that we can do this.", 'start': 2007.361, 'duration': 2.702}, {'end': 2014.566, 'text': "So I'm going to update the prompt template and I'm going to add a new decorator.", 'start': 2010.103, 'duration': 4.463}, {'end': 2026.888, 'text': 'So please explore multiple ways of solving the problem and explain each.', 'start': 2015.919, 'duration': 10.969}, {'end': 2030.05, 'text': "So that's my new prompt.", 'start': 2028.97, 'duration': 1.08}, {'end': 2031.832, 'text': "And let's see what it does.", 'start': 2030.691, 'duration': 1.141}, {'end': 2033.533, 'text': 'So the question is going to be the same.', 'start': 2031.912, 'duration': 1.621}, {'end': 2034.754, 'text': "It's the same piece of code.", 'start': 2033.593, 'duration': 1.161}, {'end': 2038.205, 'text': "So I'm just going to Do the same thing.", 'start': 2035.355, 'duration': 2.85}], 'summary': 'Using the star operator improved code efficiency and provided a more pythonic way of printing array elements.', 'duration': 70.113, 'max_score': 1968.092, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx01968092.jpg'}, {'end': 2089.195, 'src': 'embed', 'start': 2061.641, 'weight': 4, 'content': [{'end': 2064.563, 'text': 'List comprehension is a very Pythonic way of doing these things.', 'start': 2061.641, 'duration': 2.922}, {'end': 2066.704, 'text': 'Next is the enumerate function.', 'start': 2065.143, 'duration': 1.561}, {'end': 2072.487, 'text': "If we wanted to do that, it's returning an iterator that will yield the index and the value of each element in the array.", 'start': 2066.744, 'duration': 5.743}, {'end': 2073.748, 'text': 'And we can use them to print it out.', 'start': 2072.507, 'duration': 1.241}, {'end': 2076.29, 'text': 'Or if we wanted to, we could use the map function.', 'start': 2074.409, 'duration': 1.881}, {'end': 2080.272, 'text': 'The function then applies another function to each element,', 'start': 2076.67, 'duration': 3.602}, {'end': 2085.274, 'text': 'making it an interval and allows us to print each element with some kind of custom formatting if we wanted.', 'start': 2080.272, 'duration': 5.002}, {'end': 2089.195, 'text': "And then what's really nice is it gives us this table comparing the three methods.", 'start': 2085.774, 'duration': 3.421}], 'summary': 'Pythonic list comprehension, enumerate, and map functions compared for iteration and formatting.', 'duration': 27.554, 'max_score': 2061.641, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02061641.jpg'}], 'start': 1818.396, 'title': 'Code optimization and template setup', 'summary': 'Discusses setting up the generatetext helper function and using a prompt template for a python code question, as well as exploring different pythonic ways for code optimization, presenting methods such as list comprehension, enumerate function, and map function with their respective benefits.', 'chapters': [{'end': 1913.602, 'start': 1818.396, 'title': 'Setting up generatetext and code template', 'summary': "Discusses setting up the generatetext helper function using retry to handle backend calls, wrapping palm's generate text function with parameter overrides, and using a prompt template for a python code question.", 'duration': 95.206, 'highlights': ["Setting up the generateText helper function with retry for backend calls and parameter overrides The chapter discusses importing the retry to handle backend calls, using it to retry in case of failure, and wrapping Palm's generate text function with parameter overrides, such as overriding the default temperature with 0.0.", "Using a prompt template for a Python code question The speaker uses a prompt template with hardcoded priming and decorator for a Python code question, with the example of a function iterating through an array and asks whether it's good or bad Python code."]}, {'end': 2099.02, 'start': 1914.142, 'title': 'Exploring different ways of code optimization', 'summary': 'Explores the process of optimizing code by seeking help from an engine to generate text and explores different pythonic ways for code optimization, ultimately presenting multiple methods such as list comprehension, enumerate function, and map function with their respective benefits.', 'duration': 184.878, 'highlights': ['The chapter explores the process of optimizing code by seeking help from an engine to generate text and explores different Pythonic ways for code optimization.', 'The engine suggests using the star operator to unpack the array into individual arguments, which is more concise and efficient than using a for loop.', 'It presents multiple methods such as list comprehension, enumerate function, and map function with their respective benefits for code optimization.']}], 'duration': 280.624, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx01818396.jpg', 'highlights': ['Setting up the generateText helper function with retry for backend calls and parameter overrides', 'Using a prompt template for a Python code question', 'The chapter explores the process of optimizing code by seeking help from an engine to generate text and explores different Pythonic ways for code optimization', 'The engine suggests using the star operator to unpack the array into individual arguments, which is more concise and efficient than using a for loop', 'Presents multiple methods such as list comprehension, enumerate function, and map function with their respective benefits for code optimization']}, {'end': 2532.793, 'segs': [{'end': 2127.571, 'src': 'embed', 'start': 2099.1, 'weight': 1, 'content': [{'end': 2103.161, 'text': 'List comprehension is concise, but can be difficult to read for complex code.', 'start': 2099.1, 'duration': 4.061}, {'end': 2108.284, 'text': 'Enumerate is easy to read, but requires an extra variable, an extra memory to store the index.', 'start': 2103.622, 'duration': 4.662}, {'end': 2113.146, 'text': 'And the map is flexible, but the con, of course, requires that custom function to format the output.', 'start': 2108.744, 'duration': 4.402}, {'end': 2117.247, 'text': 'So there are many, many ways that you can slice this problem.', 'start': 2113.786, 'duration': 3.461}, {'end': 2118.888, 'text': 'And I think one of the really,', 'start': 2117.307, 'duration': 1.581}, {'end': 2127.571, 'text': "really nice things about being able to use a large language model is by just the tiniest changes in the prompt and being more explicit in what we're asking for.", 'start': 2118.888, 'duration': 8.683}], 'summary': 'List comprehension is concise but complex, enumerate is easy but memory-intensive, and map is flexible but requires custom functions. many ways to solve the problem with language model usage.', 'duration': 28.471, 'max_score': 2099.1, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02099100.jpg'}, {'end': 2261.762, 'src': 'embed', 'start': 2235.224, 'weight': 0, 'content': [{'end': 2240.067, 'text': 'Of these three solutions, the most Pythonic is the first one, which uses the list comprehension syntax.', 'start': 2235.224, 'duration': 4.843}, {'end': 2243.029, 'text': "This is because it's the most concise and readable solution.", 'start': 2240.467, 'duration': 2.562}, {'end': 2250.054, 'text': "So again, we're seeing that not only is it generating code, not only is it improving our code, but it can also have an opinion about that code.", 'start': 2243.51, 'duration': 6.544}, {'end': 2253.557, 'text': 'And from these opinions, maybe we can also learn to be better developers.', 'start': 2250.435, 'duration': 3.122}, {'end': 2256.84, 'text': "Okay, next let's take a look at simplifying code.", 'start': 2254.659, 'duration': 2.181}, {'end': 2258.721, 'text': 'And similar to the previous example.', 'start': 2256.92, 'duration': 1.801}, {'end': 2261.762, 'text': "it's a common scenario where you've written code that works,", 'start': 2258.721, 'duration': 3.041}], 'summary': 'Using list comprehension is the most pythonic and concise solution, improving code readability and maintainability.', 'duration': 26.538, 'max_score': 2235.224, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02235224.jpg'}, {'end': 2301.458, 'src': 'embed', 'start': 2273.408, 'weight': 2, 'content': [{'end': 2275.61, 'text': "And here's where an LLM can come to the rescue.", 'start': 2273.408, 'duration': 2.202}, {'end': 2282.036, 'text': "In the notebook, we're going to take a look at a perfectly good linked list class that's been implemented in Python.", 'start': 2276.111, 'duration': 5.925}, {'end': 2288.783, 'text': "But there's some parts of it that just maybe aren't the prettiest and a code reviewer might pick up on that and ask you to change it.", 'start': 2282.457, 'duration': 6.326}, {'end': 2294.048, 'text': "But let's see if the large language model using the Palm API can do the same thing for me.", 'start': 2289.223, 'duration': 4.825}, {'end': 2301.458, 'text': "So let's start with my simple, prompt template where I'm just saying can you please simplify this code for a linked list in Python as my primer?", 'start': 2295.034, 'duration': 6.424}], 'summary': 'Llm can simplify a linked list class implemented in python.', 'duration': 28.05, 'max_score': 2273.408, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02273408.jpg'}, {'end': 2541.098, 'src': 'embed', 'start': 2507.066, 'weight': 3, 'content': [{'end': 2510.609, 'text': 'And as well as saying explain in detail what you did to modify it and why,', 'start': 2507.066, 'duration': 3.543}, {'end': 2521.018, 'text': "I'm also going to say please comment each line in detail and explain in detail what you did to modify it and why.", 'start': 2510.609, 'duration': 10.409}, {'end': 2522.479, 'text': "So I've changed my prompt to be that.", 'start': 2521.158, 'duration': 1.321}, {'end': 2529.578, 'text': "And now if I generate my text, because the question has remained the same, let's see if it gives me a different output.", 'start': 2524.352, 'duration': 5.226}, {'end': 2532.793, 'text': "And here's my new output.", 'start': 2531.732, 'duration': 1.061}, {'end': 2541.098, 'text': "So now we can see things like, for example, I had defined my class as a node, and it's giving me a comment for that.", 'start': 2533.553, 'duration': 7.545}], 'summary': 'Modified prompt to request detailed line-by-line comments, resulting in a different output.', 'duration': 34.032, 'max_score': 2507.066, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02507066.jpg'}], 'start': 2099.1, 'title': 'Pythonic code and linked list optimization in python', 'summary': 'Discusses the advantages and disadvantages of list comprehension, enumerate, and map in python, recommending list comprehension as the most pythonic solution. it also explores the use of a large language model to simplify and modify a linked list class in python, resulting in cleaner and more efficient code.', 'chapters': [{'end': 2272.828, 'start': 2099.1, 'title': 'Pythonic code: list comprehension, enumerate, and map', 'summary': "Discusses the advantages and disadvantages of using list comprehension, enumerate, and map in python, with the language model recommending the list comprehension as the most pythonic solution due to its conciseness and readability, which aligns with developers' preferences.", 'duration': 173.728, 'highlights': ['The most Pythonic way would be to use the list comprehension syntax, which was the first one that we had earlier on, where we used print element for element in array. The language model recommends list comprehension as the most Pythonic solution due to its conciseness and readability.', "Of these three solutions, the most Pythonic is the first one, which uses the list comprehension syntax. This is because it's the most concise and readable solution. The language model emphasizes the conciseness and readability of list comprehension as the most Pythonic solution.", 'The map is flexible, but the con, of course, requires that custom function to format the output. The map is described as flexible but requires a custom function to format the output.']}, {'end': 2532.793, 'start': 2273.408, 'title': 'Simplify linked list code in python', 'summary': 'Discusses the use of a large language model (llm) to simplify and modify a linked list class in python, resulting in the creation of a cleaner and more efficient code with the help of the palm api.', 'duration': 259.385, 'highlights': ['The large language model using the Palm API is employed to simplify and modify the linked list code, resulting in a cleaner and more efficient implementation.', 'A helper function is generated by the LLM, which parses through the list values to create nodes and link the previous node to the new node, thus creating a singly linked list.', 'The prompt is modified to request the LLM to simplify the code and provide detailed comments for each line, resulting in an improved output with better readability and efficiency.']}], 'duration': 433.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02099100.jpg', 'highlights': ['The language model recommends list comprehension as the most Pythonic solution due to its conciseness and readability.', 'The map is described as flexible but requires a custom function to format the output.', 'The large language model using the Palm API is employed to simplify and modify the linked list code, resulting in a cleaner and more efficient implementation.', 'The prompt is modified to request the LLM to simplify the code and provide detailed comments for each line, resulting in an improved output with better readability and efficiency.']}, {'end': 3073.517, 'segs': [{'end': 2586.362, 'src': 'embed', 'start': 2558.63, 'weight': 1, 'content': [{'end': 2561.172, 'text': "And it's added another helper function, print out the list.", 'start': 2558.63, 'duration': 2.542}, {'end': 2570.964, 'text': 'And now in my Python, I can just do list1 equals slinklist, list1 addToHeadMon, addToHeadChew, addToHeadWednesday, and then print out the list.', 'start': 2561.932, 'duration': 9.032}, {'end': 2579.454, 'text': "As well as it's then giving us all of these details on what it's done and it believes it makes these changes to make the code more concise and easier to read.", 'start': 2571.524, 'duration': 7.93}, {'end': 2586.362, 'text': "But like I've said all along, you don't always want to fully trust the output code from something like this.", 'start': 2579.955, 'duration': 6.407}], 'summary': 'Python code updated to print list with added items, emphasizing caution in trusting automated changes.', 'duration': 27.732, 'max_score': 2558.63, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02558630.jpg'}, {'end': 2672.172, 'src': 'embed', 'start': 2618.303, 'weight': 0, 'content': [{'end': 2623.348, 'text': "But as you can see, the code it's generating is still really helping me with the linked list that I created.", 'start': 2618.303, 'duration': 5.045}, {'end': 2627.111, 'text': "It's making it much, much tidier, despite this one little error.", 'start': 2623.688, 'duration': 3.423}, {'end': 2634.117, 'text': 'And that tidiness is the kind of thing that you would be getting if you were having a human code reviewer looking over your shoulder.', 'start': 2627.591, 'duration': 6.526}, {'end': 2636.88, 'text': "But you're getting this out of a large language model now.", 'start': 2634.478, 'duration': 2.402}, {'end': 2639.25, 'text': 'As the person who wrote the code.', 'start': 2638.069, 'duration': 1.181}, {'end': 2646.354, 'text': "you're often too close to your code to really test it and figure out corner cases and other potential places where your code can fall down.", 'start': 2639.25, 'duration': 7.104}, {'end': 2655.961, 'text': 'As such, good automated testing is important and the first step in that is creating some unit tests to ensure that your code will work as expected.', 'start': 2647.075, 'duration': 8.886}, {'end': 2660.924, 'text': 'With large language models, you can take your code and ask it to generate test cases for you.', 'start': 2656.681, 'duration': 4.243}, {'end': 2665.988, 'text': 'So, in the case of the linked list that we did earlier on, where Palm simplified my code,', 'start': 2661.464, 'duration': 4.524}, {'end': 2672.172, 'text': "let's go back to the notebook and see if we can generate some test cases for all of those methods in my class very easily.", 'start': 2665.988, 'duration': 6.184}], 'summary': 'Large language models can generate test cases, aiding in code tidiness and automated testing.', 'duration': 53.869, 'max_score': 2618.303, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02618303.jpg'}, {'end': 2882.515, 'src': 'embed', 'start': 2842.803, 'weight': 4, 'content': [{'end': 2847.808, 'text': "It's not really invested in any particular algorithm or methodology and as such,", 'start': 2842.803, 'duration': 5.005}, {'end': 2852.132, 'text': 'it can give you a very fresh perspective on your code that can help you with efficiency.', 'start': 2847.808, 'duration': 4.324}, {'end': 2856.355, 'text': 'For example, when playing with palm and a binary search tree algorithm,', 'start': 2852.532, 'duration': 3.823}, {'end': 2861.478, 'text': "I use code that did the binary search tree the way I'd always learned it using recursion,", 'start': 2856.355, 'duration': 5.123}, {'end': 2867.523, 'text': 'without realizing that that might be somewhat of a gratuitous use of recursion and maybe not the best way to search a binary tree.', 'start': 2861.478, 'duration': 6.045}, {'end': 2875.469, 'text': 'An LLM can spot this right off the bat and give you suggestions about how to improve your code by maybe not using memory intensive recursion.', 'start': 2868.323, 'duration': 7.146}, {'end': 2879.973, 'text': "It's a great reminder to have LLMs as a pair programmer to explore your code.", 'start': 2875.97, 'duration': 4.003}, {'end': 2882.515, 'text': 'You never know what ideas it could generate.', 'start': 2880.454, 'duration': 2.061}], 'summary': 'Llms can provide fresh perspectives on code, such as identifying gratuitous use of recursion, leading to improved efficiency.', 'duration': 39.712, 'max_score': 2842.803, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02842803.jpg'}], 'start': 2533.553, 'title': 'Code optimization and automated unit testing', 'summary': 'Explores code optimization process with ai tool, including adding helper functions to a linked list class in python and the need for manual code review. it also discusses generating unit test cases for code using large language models and provides examples of improving algorithms for data structures.', 'chapters': [{'end': 2617.883, 'start': 2533.553, 'title': 'Code optimization and review process', 'summary': 'Explores the process of optimizing code with an ai tool, highlighting the benefits and limitations, including the addition of helper functions to a linked list class in python and the need for manual code review. it also emphasizes the importance of critically evaluating the suggested changes.', 'duration': 84.33, 'highlights': ['The AI tool provides comments and suggestions for optimizing code, such as adding helper functions to a linked list class in Python, resulting in more concise and readable code.', "The addition of helper functions, 'addToHeadMon', 'addToHeadChew', and 'addToHeadWednesday', to the linked list class in Python improves its functionality and ease of use.", 'The importance of critically evaluating the suggested changes made by the AI tool, as it may not always provide the most appropriate or accurate recommendations for code optimization.']}, {'end': 3073.517, 'start': 2618.303, 'title': 'Automated unit testing with large language models', 'summary': 'Discusses how large language models can assist in generating unit test cases for code, providing examples of generating test cases for a linked list and improving the efficiency of a binary search tree algorithm.', 'duration': 455.214, 'highlights': ['Large language models can generate test cases for code, such as creating test cases for a linked list, simplifying the process of automated testing. The large language model can be used to generate test cases for code, as demonstrated by creating test cases for a linked list, simplifying the process of automated testing.', 'Automated unit testing can identify potential areas where code may fail and help in creating unit tests to ensure the code works as expected. Automated unit testing is important in identifying potential areas where code may fail and creating unit tests to ensure the code works as expected.', 'Large language models, like the LLM, can provide a fresh perspective on code and offer suggestions to improve code efficiency, such as recommending alternative methods to avoid memory-intensive recursion in a binary search tree algorithm. Large language models, like the LLM, can provide a fresh perspective on code and offer suggestions to improve code efficiency, such as recommending alternative methods to avoid memory-intensive recursion in a binary search tree algorithm.']}], 'duration': 539.964, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx02533553.jpg', 'highlights': ['The AI tool provides comments and suggestions for optimizing code, such as adding helper functions to a linked list class in Python, resulting in more concise and readable code.', "The addition of helper functions, 'addToHeadMon', 'addToHeadChew', and 'addToHeadWednesday', to the linked list class in Python improves its functionality and ease of use.", 'Large language models can generate test cases for code, such as creating test cases for a linked list, simplifying the process of automated testing.', 'Automated unit testing can identify potential areas where code may fail and help in creating unit tests to ensure the code works as expected.', 'The importance of critically evaluating the suggested changes made by the AI tool, as it may not always provide the most appropriate or accurate recommendations for code optimization.', 'Large language models, like the LLM, can provide a fresh perspective on code and offer suggestions to improve code efficiency, such as recommending alternative methods to avoid memory-intensive recursion in a binary search tree algorithm.']}, {'end': 3432.263, 'segs': [{'end': 3162.457, 'src': 'embed', 'start': 3137.086, 'weight': 0, 'content': [{'end': 3141.867, 'text': "Now, the large language model, let's see if it's smart enough to spot this, find the bug and suggest a fix.", 'start': 3137.086, 'duration': 4.781}, {'end': 3144.648, 'text': "Let's switch over to the notebook and give it a try.", 'start': 3142.927, 'duration': 1.721}, {'end': 3150.471, 'text': "As always, I'm going to start with my prompt and my prompt template is can you please help me to debug this code?", 'start': 3145.128, 'duration': 5.343}, {'end': 3155.653, 'text': "I'll paste in the code and then explain in detail what you found and why you think it was a bug.", 'start': 3150.911, 'duration': 4.742}, {'end': 3157.114, 'text': "So I'll run this cell.", 'start': 3156.294, 'duration': 0.82}, {'end': 3162.457, 'text': "Now here's the doubly linked list, and here's the code for it that I got from an online tutorial.", 'start': 3157.614, 'duration': 4.843}], 'summary': 'Testing large language model to debug code using a prompt template.', 'duration': 25.371, 'max_score': 3137.086, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx03137086.jpg'}, {'end': 3339.208, 'src': 'embed', 'start': 3313.254, 'weight': 1, 'content': [{'end': 3320.738, 'text': "While there's no magic solution to all technical debt, there is one massive way that LLMs can be enormously helpful in overcoming it.", 'start': 3313.254, 'duration': 7.484}, {'end': 3327.382, 'text': 'Technical debt usually arises from complex code being handed down from developer to developer over time.', 'start': 3321.438, 'duration': 5.944}, {'end': 3332.805, 'text': 'You have to maintain it, but often barely understand it because it was written a long time ago,', 'start': 3327.922, 'duration': 4.883}, {'end': 3339.208, 'text': "or is overly complex or has too many dependencies that you can't change or update for fear of bringing down the entire system.", 'start': 3332.805, 'duration': 6.403}], 'summary': 'Llms can help overcome technical debt by simplifying complex code.', 'duration': 25.954, 'max_score': 3313.254, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx03313254.jpg'}, {'end': 3418.09, 'src': 'embed', 'start': 3390.378, 'weight': 2, 'content': [{'end': 3395.363, 'text': "But given that there's only one at the moment, I'm going to just use models at zero, which is that one.", 'start': 3390.378, 'duration': 4.985}, {'end': 3403.709, 'text': 'And then the final thing that I want to do just to prepare is to set up just a helper function called GenerateText.', 'start': 3397.004, 'duration': 6.705}, {'end': 3405.331, 'text': "And I'm going to do that here.", 'start': 3404.25, 'duration': 1.081}, {'end': 3407.775, 'text': "I'm going to import Google API Cores, retry.", 'start': 3405.351, 'duration': 2.424}, {'end': 3410.439, 'text': "And then on retry, I'm going to pass GenerateText.", 'start': 3407.795, 'duration': 2.644}, {'end': 3414.967, 'text': 'And GenerateText is just going to pass my prompt, my model, and my temperature.', 'start': 3410.52, 'duration': 4.447}, {'end': 3418.09, 'text': 'to palm to be able to get it to generate the text.', 'start': 3415.587, 'duration': 2.503}], 'summary': 'Preparing by using models at zero and setting up a helper function called generatetext.', 'duration': 27.712, 'max_score': 3390.378, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx03390378.jpg'}], 'start': 3074.295, 'title': "Llm's bug spotting and technical debt", 'summary': "Discusses the large language model's capability to identify bugs in code, including finding and suggesting fixes for a bug in a doubly linked list implementation, and its potential in overcoming technical debt by setting up apis and models for text generation.", 'chapters': [{'end': 3256.885, 'start': 3074.295, 'title': "Llm's bug spotting and code understanding", 'summary': "Discusses the large language model's ability to understand and identify bugs in code, demonstrating its capability to find and suggest a fix for a bug in a doubly linked list implementation obtained from an online tutorial.", 'duration': 182.59, 'highlights': ['The Large Language Model can identify bugs in code and suggest fixes, as demonstrated by its ability to find and suggest a fix for a bug in a doubly linked list implementation obtained from an online tutorial.', 'The model correctly identified the bug in the list print function, showcasing its capability to spot specific bugs in code.', 'The discussion emphasizes the importance of understanding and fixing code problems inspired by the LLM, rather than directly copying and pasting from it.']}, {'end': 3432.263, 'start': 3257.844, 'title': 'Llms and technical debt', 'summary': 'Discusses the impact of temperature on model variance, the usefulness of llms in identifying bugs, and the potential of llms in overcoming technical debt, with a focus on setting up apis and models for text generation.', 'duration': 174.419, 'highlights': ['The closer the temperature is to 0, the less likely there is going to be random variance in the model. Lower temperature reduces random variance in the model.', "With a value like 0.7, which is closer to 1, we'll be getting some variance in the model. Higher temperature (0.7) leads to increased variance in the model.", "Despite randomness, the bug is almost always found in the ListPrint method, aiding in spotting bugs and testing the model's code for bug fixing. Consistent identification of bugs in the ListPrint method despite model randomness.", 'LLMs can be enormously helpful in overcoming technical debt arising from complex code, as they aid in understanding and maintaining the code over time. LLMs can assist in understanding and maintaining complex code, potentially addressing technical debt.', 'Setting up APIs and models for text generation, including importing libraries, selecting models with desired capabilities, and setting up a helper function called GenerateText. Process of setting up APIs and models for text generation, including library imports and model selection.']}], 'duration': 357.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx03074295.jpg', 'highlights': ['The Large Language Model can identify bugs in code and suggest fixes, as demonstrated by its ability to find and suggest a fix for a bug in a doubly linked list implementation obtained from an online tutorial.', 'LLMs can be enormously helpful in overcoming technical debt arising from complex code, as they aid in understanding and maintaining the code over time. LLMs can assist in understanding and maintaining complex code, potentially addressing technical debt.', 'Setting up APIs and models for text generation, including importing libraries, selecting models with desired capabilities, and setting up a helper function called GenerateText.']}, {'end': 3842.715, 'segs': [{'end': 3539.189, 'src': 'embed', 'start': 3497.721, 'weight': 0, 'content': [{'end': 3504.325, 'text': "there's a chance that the operating system could think you're doing something naughty and that the app store could shut you down.", 'start': 3497.721, 'duration': 6.604}, {'end': 3512.811, 'text': 'so they have some very complex and very secure apis that allow you to be able to access memory on a device.', 'start': 3504.325, 'duration': 8.486}, {'end': 3521.163, 'text': 'So, as a result, this piece of code that I had to write first of all to take the RGB data of the image and to convert it into tensors,', 'start': 3513.391, 'duration': 7.772}, {'end': 3525.369, 'text': 'and then also to safely access the memory of the device, in this case an iPhone', 'start': 3521.163, 'duration': 4.206}, {'end': 3527.131, 'text': "As you can see, it's a lot of code.", 'start': 3525.849, 'duration': 1.282}, {'end': 3529.615, 'text': "I'm scrolling through it here and there's a lot of code here.", 'start': 3527.191, 'duration': 2.424}, {'end': 3532.607, 'text': 'And this to me, is a great example of technical debt,', 'start': 3530.086, 'duration': 2.521}, {'end': 3539.189, 'text': 'where if somebody had built an application that has this code in it and now you inherit this application,', 'start': 3532.607, 'duration': 6.582}], 'summary': 'Secure apis allow access to device memory, but could lead to app shutdown. example of technical debt with complex code for image processing.', 'duration': 41.468, 'max_score': 3497.721, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx03497721.jpg'}, {'end': 3730.181, 'src': 'embed', 'start': 3704.981, 'weight': 1, 'content': [{'end': 3711.466, 'text': 'in Markdown and I now have a Markdown document that I can use as the beginning of tech documentation for this class.', 'start': 3704.981, 'duration': 6.485}, {'end': 3718.052, 'text': 'So as a result, we can see like using large language models like this can be really, really handy in avoiding tech debt.', 'start': 3712.107, 'duration': 5.945}, {'end': 3720.173, 'text': "This is just one example that I've given.", 'start': 3718.372, 'duration': 1.801}, {'end': 3722.595, 'text': "I've had it explain some complex code.", 'start': 3720.193, 'duration': 2.402}, {'end': 3723.856, 'text': "I've had it document the code.", 'start': 3722.615, 'duration': 1.241}, {'end': 3730.181, 'text': "But as you can see, using the same techniques, there's so many ways that you could use this type of thing to reduce your own technical debt.", 'start': 3724.337, 'duration': 5.844}], 'summary': 'Using large language models can help avoid tech debt in documentation and code explanation.', 'duration': 25.2, 'max_score': 3704.981, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx03704981.jpg'}, {'end': 3770, 'src': 'embed', 'start': 3743.891, 'weight': 4, 'content': [{'end': 3750.413, 'text': 'Now. this was a lot of fun to work on, and Swift can be a really powerful language, but there were also some circumstances where I,', 'start': 3743.891, 'duration': 6.522}, {'end': 3754.314, 'text': 'as a relatively novice Swift developer, encountered difficulties.', 'start': 3750.413, 'duration': 3.901}, {'end': 3758.636, 'text': 'And I usually had to spend hours searching for code samples that might help me.', 'start': 3755.194, 'duration': 3.442}, {'end': 3762.957, 'text': 'Now, one such example was to pass images to a mobile neural network.', 'start': 3759.396, 'duration': 3.561}, {'end': 3770, 'text': 'In this case, I would have to convert the underlying data in the CV pixel buffer format into raw RGB data.', 'start': 3763.217, 'duration': 6.783}], 'summary': 'Novice swift developer faced challenges converting image data to raw rgb for mobile neural network.', 'duration': 26.109, 'max_score': 3743.891, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx03743891.jpg'}], 'start': 3432.683, 'title': 'Exploring technical debt and ai in swift', 'summary': 'Explores technical debt in complex swift code for ios involving tensorflow lite, and discusses using ai to document and explain swift code, highlighting the potential benefits for developers.', 'chapters': [{'end': 3578.864, 'start': 3432.683, 'title': 'Exploring technical debt in ios swift code', 'summary': 'Explores technical debt in an example of complex swift code for ios, which involves accessing memory to run a tensorflow lite model for image classification, highlighting the challenges and implications for developers.', 'duration': 146.181, 'highlights': ['The code involves accessing memory on the iOS device to run a TensorFlow Lite model for image classification, posing potential challenges with app store restrictions and requiring complex and secure APIs for memory access.', 'The example illustrates technical debt in inheriting a complex application with intricate code, emphasizing the need for thorough understanding and potential challenges for developers.', 'The code snippet showcases the necessity to convert image RGB data into tensors and safely access the memory of the device, underscoring the intricate nature of the task and the resulting extensive code.', "The chapter discusses the use of a large language model to assist in understanding the complexity of the code and the prompt template used to generate a detailed explanation of the code's functionality."]}, {'end': 3842.715, 'start': 3579.324, 'title': 'Using ai to document and explain swift code', 'summary': 'Discusses using ai to document and explain swift code, highlighting the generation of technical documentation in markdown format and the potential benefits of using large language models to reduce technical debt and improve efficiency in software development.', 'duration': 263.391, 'highlights': ['The chapter discusses using AI to document and explain Swift code, highlighting the generation of technical documentation in Markdown format. The AI is used to generate technical documentation in Markdown format, providing clear and organized documentation for the Swift code.', 'The potential benefits of using large language models to reduce technical debt and improve efficiency in software development are emphasized. Using large language models can be really handy in avoiding tech debt and reducing the need for extensive manual documentation, thus improving efficiency in software development.', 'The challenges faced by the author as a novice Swift developer and the difficulties encountered when working on iOS devices are highlighted. The author encountered difficulties as a novice Swift developer, particularly when dealing with security concerns related to passing images to a mobile neural network on iOS devices.']}], 'duration': 410.032, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/iOmy0Y30Cx0/pics/iOmy0Y30Cx03432683.jpg', 'highlights': ['The code involves accessing memory on the iOS device to run a TensorFlow Lite model for image classification, posing potential challenges with app store restrictions and requiring complex and secure APIs for memory access.', 'The potential benefits of using large language models to reduce technical debt and improve efficiency in software development are emphasized. Using large language models can be really handy in avoiding tech debt and reducing the need for extensive manual documentation, thus improving efficiency in software development.', 'The chapter discusses using AI to document and explain Swift code, highlighting the generation of technical documentation in Markdown format. The AI is used to generate technical documentation in Markdown format, providing clear and organized documentation for the Swift code.', 'The example illustrates technical debt in inheriting a complex application with intricate code, emphasizing the need for thorough understanding and potential challenges for developers.', 'The challenges faced by the author as a novice Swift developer and the difficulties encountered when working on iOS devices are highlighted. The author encountered difficulties as a novice Swift developer, particularly when dealing with security concerns related to passing images to a mobile neural network on iOS devices.']}], 'highlights': ['LLMs are used by experienced developers for error handling, performance improvements, and more.', 'The course focuses on using the Palm API to simplify and improve code, write test cases, debug, and refactor code.', 'Obtaining an API key and generative AI libraries from Google is necessary for setup.', 'The difference between chat bison and text bison models is explained, with chat bison optimized for chat scenarios and text bison optimized for single-shot prompts.', 'Creating a helper function to generate texts without repeating code is demonstrated using the retry library.', "The function 'generate text' is created to take a prompt and model as input, with the option to adjust the temperature setting for controlling randomness.", 'Using a temperature of 0.0 creates a deterministic model, ensuring consistent results for the given prompts.', 'The process of creating a generate text function involves defining prompt, model, and temperature parameters to avoid reinventing the wheel.', 'Prompting with the generate text function allows for code generation, following a pattern of creating a prompt and using it with the selected model to get a completion.', 'The output text from the model predicts the next set of tokens, such as completing a string with an answer based on the input prompt.', 'The importance of thoroughly testing output code before using it in production is emphasized.', 'The concept of using template strings to prime an LLM for a specific behavior is introduced, improving the performance of the prompt and leading to better results.', 'The process of generating code using prompts and decorators is demonstrated, showing how changing the prompt to be more specific and appropriate for coding can yield better results.', 'The prompt is composed of three parts: priming, question, and decorator, each serving a specific function in preparing and structuring the prompt.', 'The process of optimizing code by seeking help from an engine to generate text and exploring different Pythonic ways for code optimization is explored.', 'The language model recommends list comprehension as the most Pythonic solution due to its conciseness and readability.', 'The AI tool provides comments and suggestions for optimizing code, such as adding helper functions to a linked list class in Python, resulting in more concise and readable code.', 'Large language models can generate test cases for code, simplifying the process of automated testing.', 'Automated unit testing can identify potential areas where code may fail and help in creating unit tests to ensure the code works as expected.', 'The Large Language Model can identify bugs in code and suggest fixes, as demonstrated by its ability to find and suggest a fix for a bug in a doubly linked list implementation obtained from an online tutorial.', 'LLMs can be enormously helpful in overcoming technical debt arising from complex code, as they aid in understanding and maintaining the code over time.', 'The potential benefits of using large language models to reduce technical debt and improve efficiency in software development are emphasized.', 'The chapter discusses using AI to document and explain Swift code, highlighting the generation of technical documentation in Markdown format.', 'The example illustrates technical debt in inheriting a complex application with intricate code, emphasizing the need for thorough understanding and potential challenges for developers.']}