I turn AI research into systems that hold up in production, and I teach it the same way: runnable notebooks, not slide decks.
One course, six open-source repos, three books, and a weekly newsletter. Everything here is free to read and run.
Start here: Take the course · Learn for free · Read the books · Get the newsletter · Work with me
Prompt to Production is my full course on building software with AI the way professionals do: the methods behind reliable, modular, production-grade systems, taught systematically. 17 modules, each pairing a video lecture with a hands-on lab, from your first structured prompt to a working production system. Every module is live.
Your coding agent starts every session knowing nothing about your project, so it guesses. Paste one line into the agent you already have open, and about fifteen minutes later your repository has a docs layer written from the code itself, plus a card scoring what your agent knew before and after.
We ran it on six repositories you already depend on. Each one was asked five questions about itself, cold, then again after the layer was written. The last column counts statements in that project's own documentation that its own code disproves:
| repo | before | after | own docs its code disproves |
|---|---|---|---|
| fastapi | 2 of 5 | 5 of 5 | 2 |
| flask | 3 of 5 | 5 of 5 | 6 |
| django | 3 of 5 | 4 of 5 | 1 |
| express | 2 of 5 | 4 of 5 | 4 |
| requests | 2 of 5 | 4 of 5 | 0 |
| langchain | 5 of 5 | 4 of 5 | 5 |
Flask's six include four documentation examples that raise TypeError when you run them. Langchain scored lower afterwards, because it already ships a 380-line agent instruction file and the cold read was grading theirs; that row is in the table anyway.
Clone any of those repos, paste the same line, and check the number yourself. No signup.
Every repository is a curriculum. Each technique is a self-contained notebook you can open, run and adapt, with the intuition explained next to the code.
| Repository | What you will learn | Stars |
|---|---|---|
| RAG Techniques | 41 retrieval-augmented generation techniques, from chunking and reranking to agentic, graph-based and self-correcting RAG, plus an evaluation suite. |  |
| GenAI Agents | 59 agent implementations, from a single conversational bot to multi-agent and self-improving systems, across LangGraph, AutoGen and PydanticAI. |  |
| Agents Towards Production | The playbook for shipping agents: orchestration, memory, security, observability, evaluation, deployment, GPU serving and fine-tuning. |  |
| Prompt Engineering | 22 prompting techniques, from basic structure through chain-of-thought, self-consistency, prompt security and systematic evaluation. |  |
| Controllable RAG Agent | One complete reference agent: a deterministic graph that plans, decomposes questions, retrieves across stores and verifies its own groundedness. |  |
| Agent Memory Techniques | 30 notebooks on memory for agents: buffers, vector and graph stores, cognitive architectures, Mem0, Letta, Zep, Graphiti and LoCoMo benchmarks. |  |
Smaller tools: claude-watch (live observability for Claude Code sessions) · Agentic Engineering (a docs layer that gives your coding agent a memory of your project) · moltbook-agent-guard (prompt-injection scanning and guardrails for agents)
Visual, intuition-first guides. Each one turns a repo above into a book you can read end to end, with diagrams that make the tricky parts click. PDF and EPUB, free lifetime updates, and chapter 1 of each is free to read.
RAG Made Simple 🏆 Amazon Bestseller in Generative AI (hit #1 at launch) 400 pages, 22 techniques, with side-by-side comparisons and diagrams. 33% off with code RAGKING |
Agent Memory Made Simple ✨ New: the complete visual guide to memory for AI agents 467 pages on how agents remember: buffers, vector and graph memory, cognitive architectures and the frameworks in production. |
Prompt Engineering from Zero to Hero 📖 Master the art of AI interaction 22 chapters of hands-on prompting techniques. The foundation that makes RAG and agents work better. |
Want all three? The Applied AI Bundle is prompting, retrieval and memory in one purchase, $48 less than buying them separately.
- DiamantAI Newsletter: one deep dive a week on GenAI engineering, with code. Free, read by 50k+ engineers.
- Discord: 4k+ members, real-time Q&A and project feedback.
- YouTube: the tutorials, as video walkthroughs.
- LinkedIn and X: daily notes on what is new and what actually works.
- r/EducationalAI: discuss prompts, RAG and agent design.
If you build for the GenAI stack (vector databases, orchestration, memory, observability, security, evaluation), we can co-create an open-source tutorial that shows your tool inside a real, runnable workflow.
- Reach: 500,000+ developer views a month across the repositories, plus the newsletter and community.
- Format: clear, reproducible notebooks with no paywall, kept neutral and end to end.
- Track record: the teams below have already done it.
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Interested? Reach me on LinkedIn or through diamant-ai.com.
Everything above stays free because people do three small things:
- Star the repositories you use. It is how other developers find them.
- Share a notebook with your team or on social media.
- Sponsor through GitHub Sponsors or Buy Me a Coffee.