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How to move beyond simple vector search by implementing parent-document retrieval and query expansion pipelines to improve context relevance in production RAG systems.

Build a practical RAG evaluation loop with retrieval metrics, answer checks, citations, human review, judge models, and release gates.

Every team building retrieval-augmented generation reaches the same decision: which vector database? Here's how pgvector, Pinecone, and Qdrant actually behave in production.

A practical RAG evaluation checklist for app developers: test retrieval, citations, answer grounding, regressions, and release gates before shipping AI features.