Welcome to this second course on generative AI for software development. I'm here again with Laurence Barone, your instructor for these courses. In the previous course, you took a look at how generative AI works, learned some principles of prompting to help you carry out software development tasks with the help of AI, and used an LRM to help you analyze some foundational data structures and improve the performance at scale. In this course, you'll see how LRMs can grease the wheels of collaboration and make you better at all the things that can become pain points as code is passed back and forth between you and others. That's right, Andrew. And in this course, one of the things we really wanted to lean into was that theme of working with others and passing code back and forth with them. And it's going to follow three things. The first one will be testing and debugging, so helping you write test cases and helping you pass test cases off to your testing team. And then documentation, allowing you to put the proper comments and the proper formatting of comments so that things such as auto documentation tools can turn that into documentation for you. And then, of course, dependency management, everybody's best friend, so that you can work not just with third-party dependencies from outside of your company, but also how you can understand how to integrate dependencies from inside your company and from the people that you work with. The dream has always been to have well-tested, well-documented code. And the reality is we're all kind of busy and can't always meet that high standard that we aspire to and we encourage others to aspire to as well. And generally, I'm hoping a lot. Absolutely. And it's like, I mean, I'd like to share one example is that like talking about sharing code is that we've done many courses together. And by definition, when we do courses, we share code with the learners. And in one of the courses that we worked on, we were doing TensorFlow for mobile applications, and specifically for iOS mobile applications in Swift. And if you think about doing an image classification, for example, you have your image as an NS image, I believe was the data structure on iOS that you have to convert into tensors that you then pass to the TensorFlow Lite that you then get an interpretation back that you in tensors that you then have to cast back into native data types. And I wrote the code to do this for a simple image classifier. And in the three weeks between writing the code and then teaching the course, I forgot what it did. I forgot like all how these things worked. And it took me a long time to kind of sit down and like work through that and understand it again. This was before LLMs. You know, so then recently, I was like working through building this course now where we have LLMs as our pair programmer, I actually took that code, and I tried it out, and I was able to document it, I was able to get an LLM to explain it back to me. Now it's like three years later that helped me to understand it better. And it even found and fixed some issues in the code. Wow. So it's like, you know, and those are the kind of things that when you share with others, or when you inherit from others, it's amazing to have that LLM by your side to work it through with you. Yeah, I think someone said that, if you haven't looked at your own code for long enough, it's as if a stranger had written it. And this, in this case, that stranger of your younger self wrote code that he helped. Do you understand? This is great. Yep. I had to look back and tell him off that he didn't do a great job. And, you know, I find that machine learning people are notorious, not proud of this, for having complex code. I think probably because machine learning is so iterative. We try something, works, it doesn't work, change it. And by the time we finally get to working prototype, you know, the code's been iterated on many, many times, much more than traditional software engineering. And so I found that sometimes I pull documentation, you know, get a few lines of code from some cloud provider's documentation, and then a week later, I have no idea what I just did. And getting some help to understand that code, my code, someone else's code would be very welcome. I'm really glad to hear you say that. I thought it was just me. So with that, let's jump in to see how LLM can help you with testing, debugging, documentation, and dependency management, which will make you a better developer, I think, and also a better teammate to everyone you might work with. Let's go on to the next video.