Update to RAG & GenAI Copilot

{
  "description": "Improve RAG latency, throughput, token usage and cost without silently degrading correctness or security.",
  "included_files": [],
  "name": "optimize-rag",
  "skill_md_contents": "---\nname: optimize-rag\ndescription: Improve RAG latency, throughput, token usage and cost without silently degrading correctness or security.\n---\n\n\n# Optimize production RAG\n**Trigger:** High latency, high cost, throughput bottlenecks, context bloat, unnecessary reranking, index costs.\n\n1. Establish target SLO/budget and baseline by request slice. Measure p50/p95 stage times, throughput, total tokens and cost per successful answer.\n2. Attribute cost/latency by ingest/index, embedding, retrieval, rerank, context packing, generation, citation verification and retries.\n3. Change one or a small group of parameters per experiment; evaluate quality before/after on paired holdout and protected slices.\n4. Consider caching only with source version, ACL/tenant and invalidation; test for leakage. Compare top-k reduction, rerank batch sizes, compression, model choice and timeouts.\n5. Avoid optimizing average latency while harming tail latency, no-answer performance or correctness for low-frequency users.\n6. Provide ranked experiments with expected gain, quality/security risk, measurement and rollback triggers; don't invent costs or model pricing.\n\nRead `../../references/observability-slo.md` and `../../references/evaluation-metrics.md`.\n"
}