Deep Dive
Retrieval-Augmented Generation
A large language model only knows what it was trained on. RAG gives it an open book — retrieving the right facts at query time and grounding every answer in them. This series takes you from the core idea to the 2026 frontier.
TL;DR RAG = retrieve relevant text for a question, augment the prompt with it, then generate a grounded answer. It reduces hallucinations, keeps knowledge current without retraining, and adds source citations — which is why it underpins most production LLM apps.
What you'll learn
The pipeline
Every stage from raw documents to a cited answer, and where latency and cost hide.
The retrieval stack
Chunking, embeddings, vector search, hybrid (dense + sparse) fusion, and reranking.
The 2026 frontier
Agentic RAG, GraphRAG, and adaptive routing — where "retrieval" becomes "context engineering".
Chapters
Why RAG?
The three limits of a bare LLM — hallucination, stale knowledge, no private data — and how retrieval fixes them.
The RAG pipeline
The end-to-end flow: ingest → embed → store → retrieve → augment → generate, as an animated diagram.
Chunking & embeddings
How documents become searchable vectors — and why chunk size quietly decides your answer quality.
Retrieval & reranking
Dense vs sparse search, hybrid fusion, and the reranking step that lifts precision the most.
Advanced RAG
Contextual retrieval, agentic RAG, GraphRAG / LazyGraphRAG, and adaptive query routing.
Evaluation & pitfalls
Faithfulness, context precision/recall, and the failure modes every RAG system hits.