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.

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.

This course provides a very basic introduction, primarily suited for individuals with no prior coding experience. It focuses on learning Python with the assistance of ChatGPT. While it may be helpful for absolute beginners, those with some coding knowledge might find it overly simplistic. For more in-depth resources, I recommend exploring the documentation on OpenAI, Gemini cookbook, or prompt engineering pages, which offer a wealth of detailed information.

Well, it was very basic and didn't do much for me. I was kind of hoping that this will have more challenging exercises but it truly was a boring / begineer level course. Hoping for next courses to be way better. !

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.

Way below expectations

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.

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

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.

Small but useful bit of valuable knowledge, with lots of filler in between. Not a lot of focused content, and found the ChatGPT labs clunky and unwieldy.

I took the first course of the series of 3 but it really was not what I expected, at the beginning topics of how to use chatgpt to generate, improve, document code, etc. were covered - this is so simple, you don't need a course to ask gpt4 to improve an algorithm. But shortly after, I felt that in the following modules too much time was spent teaching lists, trees and graphs, it is as if out of nowhere the course completely changed its course, losing all focus, in the end to finish the course I had to present an assessment that had nothing to do with AI, although AI was used to solve it, it seemed more like a graph assessment from my computer science major than anything else, I didn't like the content of the course at all so I didn't I kept going.

One of the worst coreuses, why did you add a data structure in a coreuse that has no relation with it !!!! And the assignment is so challenging. Also, as a Java developer, I took this course. Why should you make the assignment Python-related? It's not worth your time, guys

Programming assingments: unclear assignement, environment not working

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

VERY BAD EXPERIENCE

I want in unerrol

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.

⭐⭐⭐⭐⭐ Amazing Course – AI + Coding Made Easy! This course was incredibly well-structured and exceeded my expectations! As someone without a formal CS background, I found it super valuable—not just for learning how to use Generative AI in coding, but also for picking up essential CS concepts like data structures and graph algorithms. One of the highlights was Laurence Moroney's teaching style—clear, engaging, and easy to follow. He makes even complex topics approachable and fun, which made learning AI-assisted coding a great experience. The final assignment was mind-blowing! Solving shortest path and traveling salesman problems with AI-generated code showed just how powerful these tools can be. What I loved: ✅ Laurence’s clear and engaging explanations ✅ Hands-on projects that apply AI in real-world coding ✅ Learning prompting techniques to get better AI-generated code ✅ AI-assisted solutions for complex algorithms If you’re a developer—whether beginner or experienced—this course is a must for integrating AI into your workflow. Highly recommended! 🚀 #GenerativeAI #SoftwareDevelopment #Coding #AI #Coursera

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.