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.