Learn how generative AI can accelerate coding, debugging, testing and modern software engineering workflows - two modules, nineteen lessons, all of it demonstrated on screen.
By Board Infinity · Offered on Coursera
19 short lessons, from AI foundations to AI-assisted debugging
Add it to your LinkedIn profile the day you finish
7 assignments across the two modules, including a graded quiz
A beginner-friendly route from what generative AI actually is, to using it inside a real development workflow.
Generative AI in Software Development starts at the foundations - how artificial intelligence, machine learning, deep learning and generative AI differ, how generative models compare with discriminative ones, and how the field moved from GPT to today's assistants.
From there it becomes practical. You meet the key models used in software work, explore the OpenAI, Gemini and Mistral APIs, run a demo with Python and Postman, and learn prompt engineering techniques aimed squarely at developers.
Module two puts it to work: AI-assisted code generation, writing efficient code, AI-driven debugging, then the advanced concepts - embeddings, retrieval-augmented generation and fine-tuning - before closing on human-AI collaboration, ethics and where software engineering goes next.
No prior AI knowledge is required, but basic programming skills are helpful.
If one of these is you, the course was built for exactly this starting point.
You are learning to build, and you would rather learn AI-assisted development from the start than retrofit it later.
You write code every day and want generative AI doing the repetitive parts - generation, debugging and test support.
You want a grounded view of AI-assisted engineering: what the tools do well, where they fail, and how teams work alongside them.
You keep hearing about embeddings, RAG and fine-tuning. This is where those words become things you can actually place.
These are the learning outcomes Coursera lists for this course, word for word.
Apply generative AI to code generation, debugging, and testing for faster, error-free development workflows.
Build expertise in AI-powered developer tools like GitHub Copilot, ChatGPT, and CodeWhisperer for software engineering.
Implement embeddings, retrieval-augmented generation (RAG), and fine-tuning to optimize AI-driven applications.
Evaluate ethical issues, collaboration models, and future trends in AI-powered software engineering.
A dedicated coach who ships software for a living - someone who can look at the output your AI assistant just produced and tell you whether to trust it.
Book a demo sessionCoaching is a separate Board Infinity offering. It is not part of the Coursera course, and it is not required to complete it.
The failing build you have stared at for an hour, explained by someone who has seen it before.
Not just whether it runs - whether you would want to maintain what the model wrote.
Prompting and integration problems set for you, then reviewed, so the technique turns into habit.
Which direction fits you - AI integrations, developer tooling or application work - and what to learn next.
About 4 hours end to end, fully self-paced. Every lesson, reading and assignment is listed below exactly as it appears in the course.
Lesson titles, durations and counts are taken from the course as listed on Coursera and may be updated by the instructor.
These are the tools Coursera lists for this course, plus the technologies the lessons discuss along the way.
Explored as one of the key AI models used in software development today.
Covered with the OpenAI and Mistral APIs in the module 1 API lesson.
Introduced in module 2 as a tool for AI-assisted code generation.
ChatGPT, CodeWhisperer, GPT, LLaMA, OpenAI models, Mistral, APIs, Python and Postman are discussed or demonstrated in the lessons - not all are dedicated course tools.
The eight skills Coursera lists for this course, grouped the way the work actually splits - understanding the models, applying them, and shipping responsibly.
What generative AI is, and what is actually happening underneath.
Putting models into real applications and workflows.
The tooling and the judgement that keep it trustworthy.
Complete the course and you earn a shareable certificate from Board Infinity, issued through Coursera - and Coursera's own button puts it on your LinkedIn profile in one click.
The certificate is issued by Board Infinity through Coursera. It is not a certification from any AI company or technology provider.
Earned on completion and yours to keep.
Coursera's built-in button places it under Licenses & Certifications.
Awarded for the seven assignments, including the final graded quiz.
With 8 languages available, and a fully flexible schedule.
Realistic applications of what this course covers - the day-to-day work these skills change.
Using generation and completion tools as part of how code gets written, with your own review still deciding what ships.
Letting AI surface likely causes of an error and help cover test cases, so the slow part of the loop gets shorter.
Automating the repetitive parts of development - boilerplate, documentation, refactoring passes - with prompts that hold up.
Calling model APIs from your own code, controlling what comes back, and wiring it into an application that behaves predictably.
Embeddings, retrieval-augmented generation and fine-tuning - the techniques that make an AI feature useful on your own data.
Knowing the ethical questions, the limits of the output, and where a human has to stay in the loop.
Generative AI does not replace software engineers, and this course does not claim it does. What it changes is where your time goes: less of it on boilerplate and first-pass debugging, more on the judgement calls a model cannot make for you.
The course is explicit about this: the lesson on automation versus job creation, and the one on human-AI collaboration, both frame AI as changing the developer's role rather than removing it.
This is a four-hour beginner course. It gives you the foundation for the following directions - where you go from there is up to what you build next.
Everything learners ask before enrolling, answered plainly.
It supports developers across the workflow: generating and completing code, helping detect and fix errors while debugging, supporting software testing, and automating repetitive work. The course treats it as an assistant to human engineers - the lessons on human-AI collaboration and on automation versus job creation are explicit that judgement, architecture and review stay with the developer.
Across two modules: the difference between AI, machine learning and deep learning; generative versus discriminative AI; the evolution from GPT to ChatGPT 4.0; the key models used in software development; OpenAI models; the OpenAI, Gemini and Mistral APIs with a practical Python and Postman demo; prompt engineering for developers; then AI code generation, writing efficient code with AI, AI-driven debugging, embeddings, RAG and fine-tuning, real-world implementation, human-AI collaboration, ethics and future trends. That is 19 video lessons, 2 readings and 7 assignments.
Coursera lists Gemini, Google Gemini and GitHub Copilot as the tools for this course. The lessons also discuss or demonstrate ChatGPT, CodeWhisperer, GPT, LLaMA, OpenAI models, Mistral, APIs, Python and Postman - those are covered as part of the material rather than as dedicated course tools. Board Infinity is not affiliated with or endorsed by any of these providers.
Module 2 opens with AI for code generation using GitHub Copilot, ChatGPT and CodeWhisperer, then moves to writing efficient code with AI assistance and AI-driven debugging - detecting and fixing errors. The prompt engineering lesson in module 1 is what makes the rest of it work: the quality of what you get back is mostly a function of how you ask.
No prior AI knowledge is required, but basic programming skills are helpful. The course is rated beginner for its AI content - it explains AI, machine learning and deep learning from the ground up - while assuming you are already comfortable reading and writing code.
Embeddings, retrieval-augmented generation (RAG) and fine-tuning, covered in a 15-minute lesson in module 2 - the longest single lesson in the course - and applied in the lesson on real-world project implementation using AI models. Together these are what let an AI feature work usefully against your own data.
The course includes a practical demo using Mistral with Python and Postman in module 1, and a lesson on real-world project implementation using AI models in module 2. Assessment is through 6 practice quizzes and a final graded quiz - 7 assignments in total - plus a Quick Course Check-In plugin activity.
It gives you a grounded starting point in AI-assisted software development, AI integrations, AI-powered application development, developer productivity workflows and generative AI engineering foundations. Those are directions the skills apply to - not guarantees. No course, including this one, can promise employment, a promotion or a particular salary.
Yes. Module 2 closes with three lessons on exactly that: AI and the future of software engineering (automation versus job creation), the human-AI collaboration and how it redefines the developer's role, and ethical considerations in AI development.
About 4 hours to complete, across 2 modules, 19 video lessons, 2 readings and 7 assignments. The schedule is flexible and fully self-paced, so deadlines move with you.
As soon as you enroll on Coursera. All lectures, readings and assignments are available from the start, and the course is fully self-paced - you can work through it in one sitting or over several weeks.
Full access to all course material and graded assignments, and a shareable certificate from Board Infinity on completion, issued through Coursera. Coursera's one-click button adds it to the Licenses & Certifications section of your LinkedIn profile.
Coursera offers financial aid to learners who cannot afford the fee. You apply for it on the Coursera course page - pricing, financial aid and any subscription options are all handled there, not by Board Infinity.
Board Infinity 1:1 coaching pairs you with a practitioner while you take the course.
Four hours, two modules, nineteen lessons. Enrollment, lessons, grading and the certificate all happen on Coursera.
Beginner · 4 hours · 2 modules · 19 video lessons · 7 assignments · Shareable certificate
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