RAG Retrieval Quality Engineer
Design and evaluate RAG retrieval with chunking, metadata, hybrid search, reranking, citations, freshness, and failure analysis.
RAG Retrieval Quality Engineer is a free AI skill for agent systems & llm workflows. Design and evaluate RAG retrieval with chunking, metadata, hybrid search, reranking, citations, freshness, and failure analysis. It works with Claude, ChatGPT, Cursor and is ready to use out of the box.
About this skill
RAG Retrieval Quality Engineer helps teams build retrieval-augmented generation systems that find the right evidence before generation. It defines ingestion, chunking, metadata, embeddings, hybrid search, filters, reranking, freshness, citations, evaluation, observability, and fallback behavior.
What it does
The skill analyzes documents, user questions, permissions, language, scale, and latency; designs the retrieval pipeline; creates evaluation datasets and metrics; identifies failure modes; and produces implementation and tuning guidance.
What is included
- Document and query analysis
- Chunking strategy
- Metadata and permission design
- Embedding and hybrid retrieval
- Reranking and context selection
- Citation and freshness rules
- Evaluation framework
- Monitoring and tuning plan
How to use it
1. Download the rag-retrieval-quality-engineer-SKILL.md file 2. Upload it to your AI or search workspace 3. Provide sample documents, queries, users, permissions, and current retrieval behavior 4. Add latency and freshness requirements 5. Use the design to implement and evaluate RAG
Examples
Design retrieval for an internal engineering assistant using architecture docs, runbooks, tickets, API docs, and code references.
A complete RAG retrieval plan with ingestion, chunking, metadata, tenant and role filters, embeddings, BM25 hybrid search, reranking, citation rules, evaluation queries, metrics, and failure analysis.
FAQ
What is this skill for?
Does it choose chunk size?
Can it use hybrid search?
How are permissions enforced?
Does it measure answer quality?
How is this different from general RAG architecture?
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