vector-index-tuning

• Tuning HNSW parameters
• Implementing quantization
• Optimizing memory usage
• Reducing search latency
• Balancing recall vs speed
• Scaling to billions of vectors

• 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

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.

• 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/implementation-playbook.md for detailed patterns, checklists, and templates.