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---
name: vector-index-tuning
description: Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
tags: [ai, vector-db]
---
# 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.
## 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
<!-- Source: .faos/custom/skills/ai-ml/vector-index-tuning/SKILL.md -->
