Building High‑Precision Vector Search for Document Retrieval on Databricks

For years, search technology meant one thing: type in a keyword, and the system goes hunting for an exact match. That works fine for product SKUs or error codes, but it falls apart the moment someone asks a real question. If your knowledge base is full of manuals, support tickets, transcripts, and reports, a person searching for "why does the machine shut down during startup" shouldn't have to guess the exact phrase the original author used.

This is the gap that vector search closes. Instead of matching words, it matches meaning. And on Databricks, building this kind of system is more accessible than most teams expect, once you understand the moving pieces.