vector-index-tuning

# Vector Index Tuning Guide to optimizing vector indexes for production performance. ## Use this skill when - Tuning HNSW parameters - Implementing quantization - Optimizing memory usage - Reducing search latency - Balancing recall vs speed - Scaling to billions of vectors ## Do not use this skill when - You only need exact search on small datasets (use a flat index) - You lack workload metrics or ground truth to validate recall - You need end-to-end retrieval system design beyond index tuning ## Instructions 1. Gather workload targets (latency, recall, QPS), data size, and memory budget. 2. Choose an index type and establish a baseline with default parameters. 3. Benchmark parameter sweeps using real queries and track recall, latency, and memory. 4. Validate changes on a staging dataset before rolling out to production. Refer to `resources/implementation-playbook.md` for detailed patterns, checklists, and templates. ## Safety - Avoid reindexing in production without a rollback plan. - Validate changes under realistic load before applying globally. - Track recall regressions and revert if quality drops. ## Resources - `resources/implementation-playbook.md` for detailed patterns, checklists, and templates.