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RAG Architecture & Debugging
Design, diagnose, and optimize high-performance RAG systems with an engineering-first framework.
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See it in action
You say
My RAG system is retrieving the right documents, but the LLM still keeps hallucinating details that aren't in the text. How do I fix this?
Your agent does
Your failure is at the Generation layer.
Recommended Fixes:
- Tighten Prompting: Add a strict "Only answer from context" rule.
- Temperature: Lower to 0.0.
- Source Weighting: Instruct the model to cite specific chunk IDs to force grounding.
What you get
About this skill
Your RAG pipeline is only as good as its weakest layer. This skill diagnoses exactly which layer is failing — chunking strategy, embedding mismatch, retrieval scoring, reranking, or prompt construction — and prescribes a fix with implementation steps. Covers hybrid search design, HyDE and query rewriting, context window management, and hallucination root-cause attribution. Whether you're building from scratch on Pinecone/Weaviate/pgvector or debugging a system already in production, give it your stack and symptoms and it returns a root-cause report with a prioritised remediation plan. Specify the system type (chatbot, search, knowledge base) and your stack — no vague advice, specific architectural decisions with tradeoffs explained.
Changelog
- v1.2LatestJun 26, 2026
Rewrote the skill body with real, expert RAG-engineering content. Every section now carries concrete method (architecture decision framework vector/hybrid/graph, failure-mode diagnostics retrieval-vs-generation, BM25+dense hybrid with RRF, structure-aware chunking + embedding choice, eval with recall@k/nDCG/faithfulness on a golden set, a knowledge/metadata schema, production readiness, integration patterns, latency/cost optimization, and monitoring/alerting) plus ready output templates. Replaced the generic placeholder sections and removed the Norwegian leak.
- v1.1Jun 11, 2026
Updated skill file
- v1.1Jun 11, 2026
Updated skill file
- v1.0Jun 11, 2026
Initial release
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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