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 for detailed patterns, checklists, and templates.