Further resources

RAG is a fast-moving field; treat any specific tool or model name in these docs as a snapshot and verify current best practice as you build. These categories are stable places to keep learning.

Foundational reading

• The original RAG paper (Lewis et al., 2020) — where the term and pattern come from.
• Papers behind the techniques here: HyDE, Self-RAG, Corrective RAG, RAG-Fusion, and GraphRAG.
• Vendor engineering blogs on contextual retrieval, hybrid search, and reranking often contain the most current, practical guidance.

Framework & tool docs

• LangChain / LangGraph, LlamaIndex, and Haystack documentation and tutorials.
• Vector DB docs: Pinecone, Weaviate, Qdrant, Milvus, Chroma, pgvector.
• Evaluation docs: RAGAS, TruLens, DeepEval, Phoenix.

Benchmarks

• MTEB for comparing embedding models — but always validate on your own data and domain.
• Reranker and retrieval leaderboards, checked at build time since they shift frequently.

Learn by building

The fastest way to understand RAG is to build the reference implementation against your own documents, measure it with a small golden set, and add one advanced technique at a time — keeping only what the numbers reward. Reading explains the shape; building teaches the details.

Written by Bipin Singh

Senior Full-Stack Engineer · AI & AWS. I build production RAG and LLM systems for enterprises.

Work with me