# RAG

> Build, optimize, and debug RAG pipelines with chunking strategies, retrieval tuning, evaluation metrics, and production monitoring.

- Skill: `dvcrn/rag` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add dvcrn/rag`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/rag/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: dvcrn (https://skillmd.com/u/dvcrn)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/dvcrn/rag

---


## 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 |