Free PDF — Production Reference

The RAG Architecture Playbook

2026 Edition · From PoC to Production

Every production RAG decision in one place. Chunking, embedding model choice, vector DB tradeoffs, hybrid search, reranking, query rewriting, evaluation. Real code, real benchmarks, real failure modes.

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Chunking Strategies Fixed vs semantic vs structural vs hierarchical. Which one wins for your doc type, with code.
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Hybrid Search That Works BM25 + dense + reranker. The exact pipeline that beats pure-vector on 90% of real corpora.
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Eval, Not Vibes RAGAS, ARES, custom rubrics — how to measure faithfulness, context recall, answer relevance without guessing.
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Cost Math by Scale Embedding + storage + query cost for 1M / 10M / 100M chunks. When self-hosted beats Pinecone.

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What's Inside

Ingest

  • Chunking: fixed, semantic, structural, hierarchical
  • Embedding model selection (OpenAI vs Voyage vs Cohere vs OSS)
  • Metadata extraction and filtering strategies
  • Multi-modal: PDFs, tables, images, code

Retrieve

  • Hybrid search architecture (BM25 + dense + rerank)
  • Query rewriting and expansion patterns
  • HyDE, RAG-Fusion, multi-query — when each helps
  • Vector DB shootout: Pinecone, Weaviate, Qdrant, pgvector

Augment & Generate

  • Context window packing without truncation pain
  • Citation enforcement and grounded answers
  • Refusal patterns when retrieval fails
  • Streaming, structured output, tool-calling RAG

Operate

  • Evaluation: RAGAS, ARES, custom rubrics
  • Failure modes: hallucination, stale data, drift
  • Reindexing strategy as docs change
  • Cost optimization at 1M / 10M / 100M scale