AI search combines retrieval (finding relevant documents) with generation (synthesizing an answer from them) to produce a search experience that answers questions directly rather than returning a list of links. It's the pattern behind Perplexity, ChatGPT Search, Google's AI Overviews, and every enterprise knowledge assistant.
The architecture is essentially RAG with a search UX: query → retrieve top-K documents → generate a cited answer from those documents → show sources alongside the answer. The quality differences between AI search products come mostly from retrieval quality (what sources are indexed, how well they rank), grounding (how faithfully the answer reflects the sources), and citation design (how clearly users can verify claims).
For enterprises building internal search: this is one of the highest-ROI AI applications because it makes existing knowledge instantly accessible. The most common failure mode is treating it as purely a technology problem — the content quality and maintenance of the underlying knowledge base matter more than any model choice.
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