DeepLearning.AI offers free short courses built with OpenAI, LangChain, Anthropic, and Google — the best free starting point for AI engineers. Take these in order: ChatGPT Prompt Engineering for Developers → LangChain for LLM Application Development → Building Systems with the ChatGPT API → Building RAG Agents with LLMs. Each course is 1-2 hours. Skip the traditional ML courses — focus on GenAI.
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Quick Answers
Are DeepLearning.AI courses free?
Yes — the short courses are free. Each is 1-2 hours and covers a specific GenAI topic. They're built in partnership with major AI companies (OpenAI, LangChain, Anthropic, Google, AWS). No credit card required.
Is DeepLearning.AI worth it?
For GenAI short courses: absolutely. They're free, taught by credible instructors, and cover practical skills. For the older ML specializations (deep learning, TensorFlow): only if you want to go into ML engineering, not AI engineering.
Which DeepLearning.AI course should I take first?
ChatGPT Prompt Engineering for Developers — it's the foundation for everything else. Then LangChain for LLM Application Development. Then Building Systems with the ChatGPT API. This progression matches the GenAI stack learning order.
Do DeepLearning.AI courses count as certifications?
They provide completion certificates, not formal certifications. No proctored exam, no industry-recognized credential. The value is in the practical skills learned. Add the completion to your LinkedIn but pair it with portfolio projects for real impact.
DeepLearning.AI has become the default entry point for AI engineers learning the GenAI stack. Andrew Ng's platform partners with the companies building the models — OpenAI, Anthropic, LangChain, Google, AWS — to create free, short courses that teach practical skills in 1-2 hours each. No fluff, no filler. Here's exactly which courses to take and in what order.
- DeepLearning.AI
An education platform founded by Andrew Ng (co-founder of Google Brain, former VP at Baidu, Stanford professor) that offers free short courses and paid specializations on AI/ML topics. The short courses are co-created with leading AI companies and focus on hands-on, practical skills.
Why it matters for AI engineers: DeepLearning.AI is the only platform where the companies building the models also build the courses. When OpenAI co-creates a prompt engineering course, that's a direct signal of what works. The LangChain team built the LangChain courses. Google built the Gemini courses. This is as close to "learning from the source" as you can get — and it's free.
Here are the courses ranked by value for AI engineers building with LLMs, RAG, and agents. All are free.
Course Details
1. ChatGPT Prompt Engineering for Developers — The non-negotiable starting point. System prompts, few-shot examples, chain-of-thought, structured output. Every AI engineer needs this. Co-taught by Andrew Ng and OpenAI's Isa Fulford.
2. LangChain for LLM Application Development — Introduction to the most popular LLM framework. Chains, memory, agents, tools. Taught by Harrison Chase (LangChain founder). Essential for anyone building LLM applications.
3. Building Systems with the ChatGPT API — Goes beyond single prompts into multi-step systems: classification → routing → processing → evaluation. This is how production AI applications actually work. Teaches the thinking behind LLM system design.
4. Building RAG Agents with LLMs — Covers RAG architecture end-to-end: document chunking, embeddings, retrieval, augmented generation. Built with NVIDIA. Directly applicable to the most common AI engineering pattern.
5. LangChain: Chat with Your Data — Deep dive into RAG with LangChain specifically. Document loaders, text splitters, vector stores, retrieval chains. Practical and hands-on.
6. Functions, Tools and Agents with LangChain — Advanced LangChain: function calling, tool use, agent architectures. Builds on course #2 with more sophisticated agent patterns.
7. Building Generative AI Applications with Gradio — How to quickly build UI prototypes for AI applications. Useful for demos and portfolio projects. Built with Hugging Face.
Seven free courses, 10-12 hours total, covering the entire GenAI stack from prompt engineering to agents. There's no paid alternative that delivers this much value per hour.
Week 1: Prompt Engineering + LLM APIs
Take "ChatGPT Prompt Engineering for Developers" and "Building Systems with the ChatGPT API."
Immediately after: Build something. Use the OpenAI API to create a simple tool — a text classifier, a data extractor, or a summarization chain. Don't start course #3 until you've built something with what you learned.
Week 2: LangChain Fundamentals
Take "LangChain for LLM Application Development."
Immediately after: Rebuild your Week 1 project using LangChain. Notice what the framework simplifies and what it adds in complexity.
Week 3: RAG
Take "Building RAG Agents with LLMs" and "LangChain: Chat with Your Data."
Immediately after: Build a RAG application over your own data — upload documents, embed them, build a retrieval pipeline, generate answers with source citations. This is your first portfolio project.
Week 4: Agents + Prototyping
Take "Functions, Tools and Agents with LangChain" and "Building Generative AI Applications with Gradio."
Immediately after: Build an AI agent that uses tools. Add a Gradio frontend. Deploy it. This is your second portfolio project.
Courses are inputs. Projects are outputs. Every week, spend more time building than watching. A half-finished project that handles real data is worth more than all seven courses completed with no projects built.
For specific project ideas matched to each skill level — with tech stacks and Cursor prompts to scaffold them — see our GenAI Project Ideas for AI Engineers.
Four weeks, seven courses, two portfolio projects. That's the accelerated path from zero GenAI knowledge to a working portfolio. Alternate between courses and building — never let courses pile up without projects.
DeepLearning.AI also offers traditional ML content that's not relevant for AI engineers focused on GenAI:
Skip These (Unless You're Going Into ML Engineering)
- Deep Learning Specialization — foundational ML theory. Important for ML engineers, not needed for AI engineers building with LLMs
- Machine Learning Specialization — Andrew Ng's classic ML course. Excellent content, wrong audience for GenAI engineers
- TensorFlow Developer Certificate — training custom models. Not needed if you're building with pre-trained LLMs via APIs
- AI for Everyone — too high-level for engineers. Designed for business professionals
The filter is simple: if the course is about building with pre-trained LLMs (GPT, Claude, Gemini, LangChain), take it. If it's about training models from scratch (TensorFlow, PyTorch, neural network theory), skip it — unless you want to become an ML engineer.
Excellent as they are, DeepLearning.AI short courses have gaps. Here's what you'll need to learn elsewhere:
DeepLearning.AI courses cover skills 2-6 of the GenAI stack (prompt engineering, LLM APIs, embeddings, RAG, agents). For the full path — including programming fundamentals, portfolio building, and job search — see our How to Become an AI Engineer: The Only Free Guide You Need.
For AI engineers focused on GenAI: DeepLearning.AI short courses + LangChain Academy is the unbeatable free combination. It covers prompt engineering, LLM APIs, RAG, and agents — the full GenAI stack — at zero cost.
DeepLearning.AI courses rank #1 in our complete certification ranking. For the full comparison of all GenAI certifications — free and paid — see our Best GenAI & AI Certifications in 2026.
DeepLearning.AI Courses: Key Takeaways
- 1Seven free short courses cover the entire GenAI stack — prompt engineering, LangChain, RAG, agents
- 2Start with 'ChatGPT Prompt Engineering for Developers' (90 minutes, non-negotiable)
- 3Follow the 4-week plan: courses + projects, alternating — never stack courses without building
- 4Skip traditional ML courses (Deep Learning Specialization, TensorFlow) unless targeting ML engineering
- 5Combine with LangChain Academy for complete framework coverage
- 6Courses provide completion certificates, not formal certifications — pair with portfolio projects
Frequently Asked Questions
Are DeepLearning.AI short courses really free?
Yes, completely free. No credit card, no trial period, no upsell. You get access to the course content, interactive Jupyter notebooks, and a completion certificate. Some older Coursera specializations require a subscription, but the short courses on DeepLearning.AI are free.
Are DeepLearning.AI courses up to date?
Generally yes — the GenAI short courses are recent and reflect current APIs and practices. However, AI moves fast. Some courses may reference older API versions. Always check the official OpenAI/LangChain/Anthropic documentation for the latest API syntax.
Can I get a job with only DeepLearning.AI courses?
Not with courses alone. The courses teach skills, but employers want proof that you can apply them. Take the courses, then build 2-3 portfolio projects (RAG app, agent, full-stack AI product) and deploy them. The combination of courses + projects + GitHub is what gets interviews.
How do DeepLearning.AI courses compare to bootcamps?
DeepLearning.AI covers the same GenAI topics as most AI bootcamps — for free. Bootcamps add structure, accountability, career services, and peer interaction. If you're self-motivated, the free courses + self-directed projects match or beat most bootcamps. If you need structure, a bootcamp may be worth the cost.
Should I do DeepLearning.AI courses or get a cloud certification?
Both, in this order: DeepLearning.AI courses first (free, teaches the skills), then a cloud certification (AWS, Azure, or Google) if your target companies use a specific cloud. The courses teach you how to build. The cloud cert proves you can build on a specific platform.
Researching Job Market & Building AI Tools for careerists since December 2020
Sources & References
- DeepLearning.AI Short Courses — DeepLearning.AI (2025)
- ChatGPT Prompt Engineering for Developers — DeepLearning.AI / OpenAI (2025)
- AI Engineering: Building Applications with Foundation Models — Chip Huyen (2025)