GenAI & LLMs · July 2026
Learning RAG from foundations to production
A practical, open learning path for understanding, building, evaluating, and operating retrieval-augmented generation systems.
Retrieval-Augmented Generation (RAG) connects a language model to external knowledge at answer time. Instead of relying only on what a model learned during training, a RAG system retrieves relevant evidence and uses it to ground an answer.
RAG is particularly useful when knowledge changes, must remain private, needs citations, or is too specialized to assume a general model already knows it. It is a system design rather than a single database query: ingestion, chunking, indexing, retrieval, reranking, generation, and evaluation all affect the result.
A guided path from beginner to advanced
The Awesome RAG training is organized as a practical learning path. Beginners can build a local, cited RAG assistant from first principles. Intermediate learners can explore hybrid search, query rewriting, metadata filters, reranking, and evaluation. Advanced learners can study corrective RAG, GraphRAG, multimodal retrieval, agentic RAG, and production operations.
Learn, lab, checkpoint
The RAG Learning Hub is designed around a repeatable Learn → Lab → Checkpoint loop. Each lesson combines explanation, references, practical guidance, a runnable notebook or Python example, and an interactive quiz so learners can move from concepts to tested understanding.
What production-minded RAG requires
A reliable system must preserve source identifiers and permissions, treat retrieved content as untrusted input, apply access control before retrieval, return traceable citations, support abstention when evidence is insufficient, and measure retrieval quality separately from answer quality. Monitoring should include corpus freshness, indexing failures, latency, empty retrieval, and citation coverage.
For teams building knowledge assistants, the training provides both a conceptual map and practical material for making better technology decisions. It is useful as a self-directed curriculum, a workshop companion, or a starting point for an internal RAG capability.