AI search means users ask questions and get grounded answers from your content — a retrieval pipeline (chunk, embed, index), an answer layer with
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Content chunked semantically, embedded, and indexed (vector store or hybrid with keyword) — retrieval quality is the product; the RAG discipline applies whole.
Retrieved passages composed into responses with citations, confidence thresholds, and an honest 'no good answer' path that falls back to classic results — grounded or silent, never improvising.
Search-bar takeover vs ask-AI panel, streaming responses for perceived speed, and feedback capture (thumbs, reformulations) feeding the improvement loop.
A query evaluation set with graded answers, retrieval-hit and answer-accuracy metrics, and cost-per-query engineering — the difference between a feature and a liability is measurement.
Skipping the discipline this article describes until an incident, audit, or stalled project forces it — every practice above is cheaper adopted early than retrofitted under pressure.
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