RAG with Claude

RAG lets Claude answer questions using your own documents and data — not just its training knowledge. This section covers the full RAG stack from ingestion to evaluation, all using the Claude API and Anthropic-compatible tooling.

In This Section

What is RAG

Why RAG exists — the gap between static model knowledge and live retrieval, and when RAG is the right answer.

RAG vs Fine-Tuning

When to use RAG, when to fine-tune, and when to combine both — a decision framework.

RAG Pipeline Anatomy

Ingest, chunk, embed, store, retrieve, generate — every stage of the pipeline explained.

Embedding Models

How embeddings work, which models pair well with Claude, and how to choose by task and cost.

Vector Databases

Pinecone, Weaviate, Chroma, pgvector — tradeoffs, when to use each, and integration patterns.

Advanced RAG

Hybrid search, HyDE, reranking, multi-hop retrieval — techniques that improve RAG quality significantly.

RAG Evaluation

Measuring faithfulness, relevance, and groundedness — metrics and tools for assessing RAG quality.