RAG

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name: RAG slug: rag description: Build, optimize, and debug RAG pipelines with chunking strategies, retrieval tuning, evaluation metrics, and production monitoring.

When to Use

User wants to implement, improve, or troubleshoot Retrieval-Augmented Generation systems.

Quick Reference

Core Capabilities

1. Architecture design — Select embedding models, vector DBs, and chunking strategies based on requirements
2. Implementation — Write ingestion pipelines, query handlers, and update logic
3. Retrieval optimization — Tune top-k, reranking, hybrid search parameters
4. Evaluation — Build test datasets, measure recall/precision, diagnose failures
5. Production ops — Monitor quality drift, set up alerts, debug degradation
6. Security — PII detection, access control, compliance requirements

Decision Checklist

Before recommending architecture, ask:

• What document types and volume?
• Latency requirements (real-time chat vs batch)?
• Update frequency (how often do docs change)?
• Access control needs (who can see what)?
• Compliance constraints (GDPR, HIPAA, SOC2)?
• Budget (managed vs self-hosted, embedding costs)?

Critical Rules

• Never skip access control — Filter at retrieval time, not after
• Always overlap chunks — 10-20% prevents context loss at boundaries
• Evaluate before optimizing — Build eval dataset first, then tune
• Same embedding model — Query and documents must use identical model
• Monitor similarity scores — Dropping averages signal drift or issues
• Plan for deletion — GDPR erasure requires re-embedding capability

Common Failure Patterns