Part VI — Tuning and optimization

These are the topics in this section, each on its own page with a stable path you can bookmark or share.

What topics are covered in this section?

• How does compression and quantization interact with an HNSW graph?
• What do M, efConstruction, and efSearch actually change?
• How do you build a reproducible method for tuning HNSW instead of guessing?
• Which search and graph optimizations improve HNSW without changing its architecture?

Part VI is about changing HNSW on purpose: what M, efConstruction, and efSearch do, how to tune reproducibly, which optimizations help without rewriting the algorithm, and how compression interacts with the graph.

What does disciplined tuning look like?

Guessing parameters wastes weeks. This Part replaces folklore with sweeps: fix dataset and ground truth, vary one knob, record recall and latency, and keep seeds and insertion order honest. You will learn which levers move build cost versus query cost, and when a search-graph optimization is worth the complexity.

Four chapters cover parameters, methodology, optimizations, and quantization.

How does Weaviate fit?

Weaviate exposes the same core knobs under production names and adds dynamic ef, quantization, and filter strategies. Part VI’s method applies directly to Weaviate collections: validate on your queries, then change config. Compression chapters align with Weaviate RQ/PQ/SQ/BQ guidance.

Tuning without measurement is how silent recall regressions ship.

What should you do next?

Read the parameters chapter, then the reproducible-method chapter before touching production. Apply optimizations and compression only after a baseline curve exists.

Part VI is operational control of HNSW quality and speed. Next, open “What do M, efConstruction, and efSearch actually change?” and run a small sweep on a sample before wide deploys.