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DeepLearning.AI · on Coursera

Generative AI for Software Development Specialization

4.6(3,200) on Coursera·85K enrolled
Intermediate 40 hours English SpecializationFREE
SkillsGenerative AILLM applicationsSoftware developmentPrompt integrationAI engineeringAPIs

Is this course right for you?

Our take
A DeepLearning.AI Coursera specialization built for engineers who want to actually ship LLM-powered features, not just understand the ideas.

Good for: Engineers who want to build and ship LLM-powered features.

Skip if: You cannot code or want conceptual-only content.

Across several courses it works through practical generative-AI software development — the real business of turning a model into a feature that works in production. That build-first, multi-course scope is what sets it apart from the many conceptual GenAI courses, and it's why it suits developers ready to build rather than anyone wanting a conceptual overview or who can't yet code.

Being a specialization, it's a real commitment of time across the sequence, so go in expecting a course of study rather than a quick primer. And budget for a cost beyond the platform: building and testing against model APIs is billed by the provider per use, separately from Coursera.

You can audit the courses free on Coursera; the certificate needs a subscription (about $49/month) or Coursera Plus, with financial aid available. GenAI tooling evolves quickly, so check the courses are reasonably current before relying on specific steps (as of 2026).

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About this course

This specialization picks up where general GenAI awareness courses leave off — it's built for engineers who want to actually ship LLM-powered features, not just understand the concepts. Across several courses, you work through prompt engineering patterns, retrieval-augmented generation (RAG), fine-tuning open models, evaluation strategies, and the engineering tradeoffs of deploying generative AI in production software.

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Instructor

DT
DeepLearning.AI Team
Coursera instructor
85K+ learners9 courses4.6 instructor rating

Taught by DeepLearning.AI's instructor team, the organization founded by Andrew Ng that has trained millions of learners through its Deep Learning and Machine Learning Specializations on Coursera.

Frequently asked questions

A broad, largely non-technical audience — business leaders, professionals, and even developers new to AI who want a genuine understanding of generative AI rather than hype. From DeepLearning.AI and Andrew Ng, it is pitched so a non-technical person can follow it while still giving developers a useful foundation. If you want to understand what generative AI is, does, and means for work, it is aimed at you.

No. It is designed to require no coding and no prior AI knowledge, focusing on concepts, use cases, prompting, and the implications of generative AI rather than building models. That makes it accessible to anyone, but also means it will not teach you to develop AI applications yourself — for that you would move on to a hands-on, code-based engineering course afterward.

How generative AI works at a conceptual level, what it can and cannot do, common real-world use cases, how to think through a generative-AI project from idea to launch, effective prompting, and the business and societal impact including risks and responsible use. It aims to give you a clear, defensible mental model and the vocabulary to make sensible decisions about AI at work.

Not really, beyond any subscription for the certificate. Because it is conceptual rather than hands-on, it does not require paid API access or cloud compute the way a build-focused course would. You can audit the material free; the graded elements and certificate need a Coursera subscription, with financial aid available. So the only real cost is optional, unlike heavier engineering courses.

Yes, you can audit it on Coursera to watch the lessons at no cost, which covers the teaching. The graded quizzes and the shareable certificate need a subscription. Given it is a concise, concept-focused program, auditing gives you nearly all the value if you want the understanding rather than the credential — a good option to sample Andrew Ng's plain-English take on generative AI.
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