Probably you as software engineer that already is using some AI chatbot know 90% of the content. Examples are on arrays, linked lists... It is missing the use of integrated AI in the IDE, to automate and skip the copy-paste. It is missing how to use AI for legacy code.

This is an excellent introductory course, as this approach represents "the next generation" of software development, applicable to all of software engineering disciplines and levels using LLM

Another fantastic course by Laurence Moroney. I really liked his thoughts on getting inspiration from LLM responses while coding. Looking forward to completing the specialization.

A decent course, although I feel that I only learned a couple of things that I hadn't already figured out from my own limited experimentation using LLMs for coding. The final assignment had one difficult bit that really required me to work with the LLM to get a better solution, so that was a good challenge.

Don't waste your money. It claims to teach you how to "effectively" use LLMs to boost productivity. However, if you're already a developer, you can easily accomplish everything covered here on your own.

Really not good. Couldn't get past the first page of the course.

Way below expectations

VERY BAD EXPERIENCE

I want in unerrol

Each course in the "Generative AI for Software Development" curriculum is well thought out, tested, paced well, and graded automatically. There will be many new concepts for someone who is new to computer science or python and software development, but that's what the LLM is there to help with! I gained a deeper appreciation for how to use / partner with an LLM, and throughout the 3 courses, I learned many new Python skills around documentation generation, unit testing, dependency management, and database manipulation. As an experienced software engineer, I found some of the lectures tedious and problems simple, but partnering with an LLM felt fresh and new and was critical to completing exercises in a timely manner (often with a one-shot response!), especially when I didn't know where to start or how to get unstuck. I must stress, that _I still learned a ton_, even though the LLM did some heavy lifting. For example, I knew about Python's `requirements.txt`, `pip install`, and `conda`. But I learned from the LLM some differences in how `pip` and `conda` manage dependencies that makes a smarter `conda` user today. I also learned from the course lectures about Python docstring formats and tools for HTML generation, among other subjects. This course starts with the prompt-engineering practice of assigning a role, being specific, providing examples, iterating on a solution, and maintaining a healthy amount of skepticism about the LLM's fallibility (usually because of how I prompted it), and throughout it provides a good palette of domains in which to practice all of this. **Once you get comfortable with how to talk to an LLM and become familiar with its strengths and weaknesses, using one feels a bit like "cheating" on a math test with a calculator—it's a game-changer for the scope of problems you can solve!** The LLM provides all kinds of great insights and know-how: it handles syntax adeptly, provides perfect algorithmic implementations, and handily recommends and employs libraries and techniques that would have just eaten up my time to discover, let alone put to use. I'm grateful for how Laurence Moroney and his team have distilled these skills into meaningful steps for all to learn and practice at their own pace.

I strugeled with the last medium graph exercice of week 3 and I found a solution (thanks to LLM proposal of using Ant colony Algorythm) Best cost: 2749, Best path: [0, 145, 279, 237, 159, 166, 59, 252, 108, 54, 218, 201, 35, 45, 140, 103, 24, 261, 71, 31, 16, 116, 34, 94, 247, 210, 278, 70, 148, 62, 171, 288, 15, 204, 83, 22, 100, 93, 152, 219, 296, 14, 25, 183, 269, 205, 154, 105, 253, 259, 113, 61, 236, 160, 53, 55, 163, 5, 134, 112, 241, 193, 7, 51, 3, 293, 2, 211, 208, 101, 110, 292, 104, 20, 81, 87, 111, 286, 268, 96, 230, 129, 250, 242, 123, 115, 97, 179, 144, 267, 50, 121, 276, 295, 102, 209, 158, 84, 284, 69, 270, 139, 189, 127, 254, 187, 180, 4, 124, 65, 285, 82, 141, 212, 176, 177, 272, 117, 184, 28, 257, 114, 221, 191, 150, 143, 9, 200, 33, 85, 192, 165, 1, 138, 265, 156, 133, 178, 6, 39, 151, 38, 27, 182, 249, 80, 206, 49, 63, 130, 294, 90, 64, 21, 92, 282, 195, 226, 203, 66, 106, 40, 213, 46, 162, 248, 251, 79, 199, 169, 36, 58, 155, 18, 88, 274, 52, 132, 222, 194, 173, 68, 157, 126, 280, 196, 185, 67, 76, 283, 223, 181, 153, 17, 149, 118, 135, 174, 30, 131, 235, 122, 146, 161, 119, 109, 273, 13, 72, 73, 291, 225, 91, 89, 190, 243, 281, 260, 86, 290, 256, 207, 262, 234, 142, 56, 137, 299, 170, 77, 19, 277, 238, 147, 47, 289, 98, 232, 224, 32, 297, 8, 107, 264, 244, 172, 258, 228, 41, 74, 271, 75, 60, 198, 29, 202, 12, 42, 229, 26, 128, 168, 214, 164, 57, 245, 220, 233, 275, 136, 44, 287, 266, 10, 43, 125, 263, 215, 240, 78, 23, 95, 120, 298, 216, 197, 99, 231, 48, 255, 188, 186, 167, 175, 246, 37, 227, 217, 11, 239, 0] way better than the one proposed (3855) but unfortunately not in the required time of 1.5s. Anyway, I was really pleased to follow this course, and I will try to use the good advice to always scrutinize the LLM results.

This course is an excellent introduction to leveraging generative AI in software development. Laurence Moroney’s deep knowledge and genuine passion for the subject make the learning experience engaging and insightful. The course covers practical skills like integrating generative AI into every stage of the development process, from design to deployment. One of the highlights was learning how to optimize code quality using large language models (LLMs), whether for debugging or experimenting with new features. The hands-on labs were challenging but highly rewarding, pushing me to think differently about how to leverage LLMs. You’ll also learn how to prompt effectively, assign LLMs specific roles, analyze code with AI, and even pair-code with them to accelerate iteration and prototyping. Laurence’s approach ensures you not only understand how LLMs work but also how to apply them effectively in real-world scenarios. Highly recommended for developers looking to enhance their coding process with AI-driven tools!

I really enjoyed this introduction. The concepts are very well presented and organized. It starts with an introduction to several AI concepts, especially the groundbreaking transformer architecture and what generative AI is. Then, we learned some techniques for better prompting and how can an LLM help with tasks like documentation, scaling, testing the code, and others. Finally, we implemented these techniques for the different data structures. I particularly enjoyed the graded lab. It was an excellent application of the concepts learned so far. I'm a beginner Python programmer and, although some of the advanced programming concepts are difficult to understand for me at this point, I believe this course was quite helpful and made me want to learn more about Python and programming in general.

it is a great course for those who are new to using AI for software development or even try to learn new programs/software development using AI both as an assistant and instructor. could have showed different AI models and focused more on local open source models such as Llama 3 rather than just using chatgpt. other than that, touching upon practical ways for fine tuning llms for software development (here the local models will have a much greater impact) could be very useful. say, I want to learn and use a niche scientific software for drug design and available AI models have a poor understanding of it. how can I fine-tune the model such that it masters the niche program and helps writing codes using it. hopefully, this is something available in the next courses?

I recently completed this course, and I found it to be an excellent resource for software engineers. The course effectively covers essential concepts of generative AI and its practical applications in software development. One of the highlights was learning prompt techniques to maximize the utility of large language models (LLMs). The hands-on exercises allowed me to practice pair-coding with LLMs, which significantly improved my coding efficiency. I highly recommend this course to software engineers looking to enhance their skills and stay competitive in the tech landscape. It provides a great blend of theory and practical application, equipping participants with valuable tools for daily development tasks.

I enjoyed the course. Although I knew about prompting and products like Co-pilot, the course showed me examples and gave me reasons for improving my skills and showing the worth of using LLMs as a coding partner. Moreover many of the techniques and tools (for example Sphinx) were on my to-do list. At times I was a little frustrated but reading the forum ensured me there were solutions. The cutting-and-pasting to and from Juypter notebooks was clumsy. Perhaps my big recommendation would be to create unittests that could be run locally, independent of the grader.

I had an excellent learning experience. The course material is well-structured, providing a clear and logical progression through each topic. The content is thorough yet easy to follow, and it’s evident that a lot of thought went into organizing it. The tutor Laurence Moroney is exceptional at explaining complex concepts in a way that’s both engaging and understandable, which made a huge difference. One small suggestion would be to include one or two more graded programming assignments. They are always fun and a great opportunity solidify the concepts further.

This is exactly what I needed as a dev working with AI. Instead of theoretical concepts, Laurence focuses on practical applications - how to effectively use LLMs as coding partners. The hands-on exercises actually reflect real dev scenarios, not just toy examples. The progression from basic prompts to advanced patterns is well structured, and you'll learn how to leverage AI tools while maintaining your developer expertise. Worth it if you want to go beyond just using ChatGPT (or other preferred GenAI tool) as a code reference.

This course was a game-changer, especially who doesn't come from a hardcore tech background. How to leverage Large Language Models (LLMs) for software development, opening my eyes to the incredible possibilities of automating tasks, generating code, and even debugging with the help of AI. I highly recommend this course to anyone looking to explore the potential of generative AI and its applications in software development. It's a fantastic investment in your professional growth and future success.

Great course, to deepen the knowledge on how to effectively use LLM across the whole Software-development cycle. All examples used are very engaging and I couldn't stop to play around with the provided code in company with the LLM. I want to highlight the coding assignments, which were challenging, but engaging at the same time!

The course re-orients how software development will be different with the use of Generative AI. In the process, I have re-learned how software development is done with Generative AI because it is a major step change. For the first course, even beginners would be able to complete the course.

A wonderful course that is just enough to get you going with Gen AI for your development needs. Clearly articulates on how you as a software engineer should position yourself to a Gen AI platform and amplify your productivity. 10x engineer is so old, with Gen AI, it should be more :)

This is a great practical introduction to leveraging generative AI and LLMs in the software development process. Through interaction and active dialog with the LLM, you learn how to go from a basic idea to more complex and correct programming functions.

Very well organized. Perfect for my level of expertise (I am just learning python). Very good to understand the basics of the usage of LLM for writing code. It is not only a matter to ask LLM to do the work, it's much more. Human only is in control.

Excellent Course. I had it on 1.5 speed the whole time. It really gave me some insight into how best interact with ChatGPT in oder to develop software. It also gave me a refresher on some basic algorithms. Really cool. Thanks!