🎁 Get the FREE AI Skills Starter Guide β€” Subscribe β†’
BytesAgainBytesAgain
BytesAgain β€Ί Skills β€Ί RAG
πŸ¦€ ClawHub

RAG

by @ivangdavila

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

Versionv1.0.0
Downloads3,280
Installs19
Stars⭐ 3
#clawhub
TERMINAL
clawhub install rag

πŸ“– About This Skill

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

| 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 β€” Select embedding models, vector DBs, and chunking strategies based on requirements 2. Implementation β€” Write ingestion pipelines, query handlers, and update logic 3. Retrieval optimization β€” Tune top-k, reranking, hybrid search parameters 4. Evaluation β€” Build test datasets, measure recall/precision, diagnose failures 5. Production ops β€” Monitor quality drift, set up alerts, debug degradation 6. Security β€” 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 β€” Filter at retrieval time, not after
  • Always overlap chunks β€” 10-20% prevents context loss at boundaries
  • Evaluate before optimizing β€” Build eval dataset first, then tune
  • Same embedding model β€” Query and documents must use identical model
  • Monitor similarity scores β€” Dropping averages signal drift or issues
  • Plan for deletion β€” 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 |

    ⚑ When to Use

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