Vector Index Tuning

If you're running vector search at scale and need to squeeze out better latency or recall without blowing your memory budget, this walks you through the tuning process systematically. It covers HNSW parameter sweeps, quantization trade-offs, and the metrics you should track before changing anything in production. The playbook approach is solid: establish baselines, benchmark on real queries, validate on staging. One thing to note is that it assumes you already have workload data and ground truth, so if you're still in the exploratory phase or don't have production metrics yet, you'll want to get that sorted first before diving into parameter optimization.