AI Learning Guides
Comprehensive, free guides to AI, LLMs, RAG, and agents — each an overview plus a set of interactive lessons. Or generate your own.
AI Agents: A Complete Guide
How AI agents observe, decide, act, and use tools — from the ReAct loop to multi-agent systems.
AI Agent Frameworks: LangChain, LangGraph, CrewAI and More
Pick the right scaffolding — LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, OpenAI Agents SDK.
RAG (Retrieval-Augmented Generation): A Complete Guide
Ground an LLM in your own documents — embeddings, vector search, chunking, reranking, evaluation.
Large Language Models (LLMs): A Complete Guide
How LLMs work — tokens, attention, context windows, prompting, function calling, fine-tuning.
AI and Machine Learning Foundations
The groundwork under LLMs — ML types, neural networks, backprop, gradient descent, embeddings.
Generative AI: Images, Audio, and Multimodal
Beyond text — diffusion models, Stable Diffusion, Whisper, text-to-speech, vision-language models.
Evaluating and Observing LLM Apps
Catch regressions before users do — evals, golden datasets, LLM-as-judge, CI gating, tracing.
AI Infrastructure: Serving, Scaling, and Cost
Run models in production — vLLM, Ollama, gateways, quantization, GPUs, cost and latency.
AI Safety, Security, and Governance
Ship AI safely — prompt injection defense, guardrails, OWASP LLM Top 10, bias, privacy, the EU AI Act.
Build AI Projects: Hands-On Guides
Learn by building — RAG apps, tool-calling chatbots, agents, eval harnesses, model serving.