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RAG & Vector Databases

Research vector databases, RAG evaluation, semantic search infrastructure, and retrieval architecture decisions.

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How to evaluate rag & vector databases

RAG and vector database choices affect answer quality, latency, permissions, and infrastructure cost. The best search backend is the one that retrieves the right authorized context for your real queries.

What to compare

  • Whether metadata filters, tenant isolation, and deletes are reliable enough for permission-sensitive products.
  • How well hybrid search handles exact terms, product names, error codes, and semantic intent.
  • Whether engineers can inspect retrieved chunks, scores, filters, and freshness when answers fail.

Buyer checklist

  • Build a gold query set from real user questions before selecting infrastructure.
  • Measure retrieval quality separately from generated-answer quality.
  • Test updates, deletes, and permission changes as first-class evaluation cases.

Main risk

A strong language model cannot fix weak retrieval. If the wrong chunks enter context, the answer may sound polished while being unsupported.

4 guides

Practical research for this topic

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PG

RAG & Vector Databases

pgvector vs Dedicated Vector Database

Decide whether PostgreSQL with pgvector is enough for RAG or whether your application needs a dedicated vector database.

pgvector vs vector databasePostgreSQL vector searchRAG database
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VDB

RAG & Vector Databases

Pinecone vs Weaviate vs Qdrant

Compare Pinecone, Weaviate, and Qdrant for RAG applications by hosting model, filtering, hybrid search, cost, and operations.

Pinecone vs WeaviateQdrant comparisonvector database pricing
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R

RAG & Vector Databases

RAG Evaluation Checklist

A practical checklist for measuring retrieval quality, answer grounding, citation accuracy, and production failure modes.

RAG evaluationretrieval qualitycitation accuracy
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V

RAG & Vector Databases

Vector Database Comparison for RAG

Compare vector databases and search backends by retrieval quality, hybrid search, filtering, latency, scale, and operational cost.

vector database comparisonRAGsemantic search
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