Update to RAG & GenAI Copilot

{
  "description": "Design and optimize search, metadata filters, hybrid retrieval, reranking, query rewriting and context selection.",
  "included_files": [],
  "name": "design-retrieval",
  "skill_md_contents": "---\nname: design-retrieval\ndescription: Design and optimize search, metadata filters, hybrid retrieval, reranking, query rewriting and context selection.\n---\n\n\n# Design retrieval and ranking\n**Trigger:** Poor search relevance, keyword mismatch, top-k instability, reranker regressions or retrieval stack trade-offs.\n\n1. Define target relevance from labeled query/document examples; include hard negatives, exact identifiers, paraphrases and no-answer questions.\n2. Inspect lexical, vector and hybrid candidates separately; record which required evidence is absent at candidate generation vs reranking.\n3. Check query rewrite/routing, metadata filters, ACL constraints, score interpretation and reproducible retrieval parameters.\n4. Compare candidate recall@k, rank-sensitive NDCG/MRR and context utilization on the same held-out set. Keep retrieval evaluation separate from generation.\n5. Only add rerankers, expansion, hybrid fusion or extra retrievers where an ablation supports net value; account for latency and cost.\n6. Ensure no reranker or query rewrite can bypass source-level authorization; preserve source IDs through context packing.\n7. Return proposed search flow, tuned controls, failure-mode tests, evidence metrics and rollback criterion.\n\nRead `../../references/retrieval-ranking.md` and `../../references/evaluation-metrics.md`.\n"
}