AI Engineering
Learn LLMs, RAG, agents, secure tools, and production AI through practical insurance examples.
Why use a vector index?
It makes repeated similarity queries efficient over a growing corpus. Small datasets can use exact comparison, but larger collections need indexing and careful metadata filtering for relevance and eligibility.
Exact versus approximate search?
Exact search examines eligible vectors thoroughly; approximate search trades some nearest-neighbor recall for speed and scale. I benchmark with realistic filters, corpus sizes, and query patterns rather than assuming ANN is always required.
Dedicated vector store or existing database?
I first test the existing database's vector capability against filtered recall, latency, lifecycle, security, and cost needs. A dedicated store is justified when it solves measured limitations enough to offset another operational dependency.
Unauthorized documents are removed after top-k, leaving no results. What is wrong?
The retrieval process selected candidates from an ineligible corpus. I move supported eligibility filters into retrieval and recheck object permissions before evidence reaches the model. Post-filtering alone can both underfill results and expose data upstream.
Difficulty is an editorial estimate. Completion means you chose to mark a question as understood.