Someone spent a week compiling 18 free AI resources from Anthropic, OpenAI, and Google. Zero dollars. And honestly, it beats most $300 courses I’ve seen.
Because here’s the thing: paid AI courses in 2026 are mostly repackaged official docs with a production budget. The old way is paying $200 to watch someone read OpenAI documentation to you for 40 hours. The actual way is just… going and reading it yourself.
I know that sounds too simple. But the list this person put together makes clear what to read and in what order. And when you have a clear reading order, you don’t need a course. You need a browser tab and a couple of focused hours. The $300 course exists because most people don’t know where to start, so they pay for the starting point. That’s all they’re buying. The starting point.
Once you know where to start, the rest is just doing the work. And the work is free.
📚 The best free resources right now
- Anthropic Prompt Engineering Guide: covers system prompts, chain-of-thought, few-shot patterns, XML tags. Takes about an hour. Changes how you think about prompting immediately. The section on XML tags alone is worth the read because it explains why structuring your prompt with clear delimiters produces dramatically more consistent outputs. Most people skip this and wonder why their prompts work sometimes but not always. This is why.
- OpenAI Cookbook 🔬: runnable Jupyter notebooks built by OpenAI engineers. RAG implementations, structured outputs, function calling. Actually runnable. Not theory. The RAG notebook alone walks you through chunking, embedding, retrieval, and response synthesis in one sitting. You could watch 10 hours of course content about RAG or you could open the notebook and build one in 90 minutes. The notebook wins every time.
- DeepLearning.AI Short Courses 🎓: co-created with OpenAI and Anthropic. You write real code in your browser while the lesson runs. It’s weirdly good. The format forces you to actually type the code rather than watch someone else type it, which sounds obvious until you realize how few online courses do this. The “Building Systems with ChatGPT API” course is a solid starting point if you’re building anything with multiple AI calls in sequence.
- Anthropic Prompt Library: ready-to-use prompts for real tasks. Don’t just copy them. Study the structure: role definitions, explicit formatting, how they constrain the output. That’s where the actual learning is. Take the “Data Organizer” prompt and look at how it specifies the exact output format it wants. Then take the “Python Bug Buster” prompt and notice it tells Claude what persona to adopt before even stating the task. These aren’t accidents. They’re patterns you can steal and apply to everything you build.
How to actually use these (instead of just bookmarking them)
- Start with the Anthropic Prompt Engineering Guide. One hour. Actually read it. Take notes on the three structural patterns: role, instruction, format. Every prompt you write from now on should have all three.
- Pick 5 prompts from the Prompt Library and reverse-engineer why they work. Copy each prompt into a doc. Then write one sentence explaining what role it gives the model, what the explicit instruction is, and how it constrains the output. Do this five times and you’ll understand prompt structure better than most people who took a paid course.
- Open one DeepLearning.AI course that matches what you’re building. Run the notebook. Don’t just read it, actually run each cell, change one parameter, see what breaks. Changing something and watching the output change is where real understanding happens.
- Use the OpenAI Cookbook when you hit a specific implementation wall. Don’t start there. Use it as a reference when you have a real problem to solve, like when you need to implement function calling and can’t figure out the schema format. Search the cookbook for your specific problem and work from the existing notebook.
That’s the whole curriculum. The pattern to internalize is this: role definitions, explicit instructions, constrained output format. Every high-quality prompt does those three things. The free resources above teach it better than any paid course because they show you real examples from real engineers, not someone’s slide deck summary of those examples. There is a meaningful difference between reading documentation written by the people who built the model and reading a course creator’s interpretation of that documentation. One is the source. The other is a copy of a copy.
The source is free. Always has been.
Save the $300 for API credits. You’ll learn more from running 500 experiments than from watching someone else run them! 🛠️
Building an AI side project? Don’t pay for AI courses. Here are 18 free developer resources from OpenAI, Google, and LangChain.
by u/Exact_Pen_8973 in PromptEngineering