vector-index-tuning

version	4.1.0-fractal
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.

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.

🧠 Knowledge Modules (Fractal Skills)

1. implementation-playbook