Rag

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

Topic	File
Pipeline components & architecture	architecture.md
Implementation patterns & code	implementation.md
Evaluation metrics & debugging	evaluation.md
Security & compliance	security.md

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

Symptom	Likely Cause	Fix
Wrong docs retrieved	Query too vague, poor chunks	Query expansion, smaller chunks
Relevant doc missed	Not indexed, low similarity	Check ingestion, hybrid search
Hallucinated answers	Context too short	Increase top-k, better reranking
Slow responses	Large chunks, no caching	Optimize chunk size, cache embeddings
Inconsistent results	Non-deterministic reranking	Set seeds, use stable sorting