Ask AI first extracts intent, complexity, and 1–4 search terms. It then routes across metadata, BM25, Vectorize, and RRF; a retry adds Critic gaps and disables the first-pass-only BM25 short circuit.
Hands-on parameter choices and response structures for web_search (ten adapters, RRF merge, dual-provider default), web_fetch (format/render strategies, finalUrl provenance), and web_extract (CSS selector determinism, no implicit LLM step), with verifiable error boundaries.
Vector search handles semantics; BM25 handles keywords. Combining them with RRF is what lets you handle both fuzzy queries and exact terms at the same time.
A single vector search on a complex query often misses relevant documents. Let the LLM rewrite the query into 3-5 sub-queries, run them in parallel, and recall improves significantly.
BM25, vector search, HyDE, and Multi-Query each produce separate result sets -- how do you merge them sensibly? RRF uses ranks instead of scores, sidestepping the fundamental problem that scores from different systems are incomparable.