RAG · Guide
RAG (Retrieval-Augmented Generation): A Complete Guide
Ground an LLM in your own documents — embeddings, vector search, chunking, reranking, evaluation.
Retrieval-Augmented Generation (RAG) grounds a language model in your own documents by retrieving the most relevant passages at query time and passing them to the model as context, instead of relying on its trained-in memory. This guide covers embeddings, vector search, chunking strategies, hybrid retrieval and reranking, RAG evaluation, and building a RAG chatbot end to end.
Generate your own lesson →What you'll learn
- Chunking Strategies for RAG
- Embeddings and Vector Search
- Reranking for Better Answers
- Hybrid Retrieval and RRF
- pgvector for RAG
- RAG Evaluation and Faithfulness
- What is RAG?
- Graph RAG
- LlamaIndex for RAG
- Pinecone Vector Database: Complete Tutorial
- Vector Databases Compared
Lessons in this guide (11)
Chunking Strategies for RAG
Split documents so retrieval finds the exact evidence an answer needs.
Embeddings and Vector Search
Turn questions and documents into searchable geometry.
Reranking for Better Answers
Use cross-encoders and second-stage scoring to improve context quality.
Hybrid Retrieval and RRF
Combine vector meaning with keyword exactness using reciprocal rank fusion.
pgvector for RAG
Use Postgres-native vector search for grounded AI apps.
RAG Evaluation and Faithfulness
Measure retrieval, grounding, and answer quality separately.
What is RAG?
Learn the open-book pattern for grounding model answers in retrieved sources.
Graph RAG
Use entities and relationships when chunk search is not enough.
LlamaIndex for RAG
Build document loaders, indexes, retrievers, and query engines with LlamaIndex.
Pinecone Vector Database: Complete Tutorial
Build and deploy a vector search application with Pinecone by mastering index creation, embedding ingestion, similarity querying, metadata filtering, and RAG pipeline integration.
Vector Databases Compared
Choose between pgvector, Qdrant, Milvus, Weaviate, Chroma, and LanceDB.
Frequently asked questions
What is RAG?
RAG stands for Retrieval-Augmented Generation. It fetches relevant chunks from your document store and feeds them to the LLM as context so answers are grounded in your data and can be cited — without retraining the model.
RAG vs fine-tuning — when do I use each?
Use RAG when the knowledge is large, changing, or must be cited (docs, policies). Use fine-tuning to change the model's behavior or format, not to add fresh facts. They're often combined.
How does vector search work?
Each chunk and the query are encoded into vectors by an embedding model; the store returns the chunks whose vectors are nearest to the query's (cosine similarity). Hybrid search adds keyword matching to catch exact terms.