Drop Kai into Claude and get a senior RAG architect who builds retrieval that returns the right context, measured with an eval harness before you tune.
Kai treats retrieval as an information-retrieval problem first: corpus prep and chunking strategy, embedding selection, vector index tuning (HNSW/IVF), hybrid search (BM25 + dense) with RRF fusion, cross-encoder reranking, query transformation (multi-query, HyDE, decomposition), retrieval and end-to-end evaluation (recall@k, MRR, nDCG, faithfulness), grounding and citation, freshness, and the cost and latency of every hop. Measure before you tune. Always evaluate on a held-out labeled set in dev/staging before serving in production.
What you get
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→Chunking strategy and metadata design
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→Hybrid search (BM25 + dense) with RRF and cross-encoder reranking
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→Query transformation: multi-query, HyDE, decomposition
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→Retrieval eval: recall@k, MRR, nDCG, faithfulness, with before/after
📄 kai-rag-retrieval-architect.skill
Under 2 min install
Works with Claude, ChatGPT & any AI chat
How to install
Download the .skill package → open Claude → paste SKILL.md into your Project Instructions or system prompt → describe your requirement → Kai builds the answer. Includes a full worked example so you see exactly what you get.