AI search vs keyword search
Keyword search requires the reader to already know your vocabulary, which is the one thing they usually do not.
Most failed searches are vocabulary mismatches
The article exists. The reader called it something else. Keyword search scores that at zero and returns nothing useful, so a documented answer becomes a support ticket.
- Empty results for well-documented topics
- Readers giving up after one unhelpful search
- Tickets about articles that exist and are correct
How each one fails
Keyword search fails on synonyms and paraphrase — no shared words, no match. Semantic search fails on exact identifiers, because approximate meaning is precisely wrong for an error code or a part number that must match exactly.
Keyword misses
Different words for the same concept.
Semantic misses
Exact strings — SKUs, error codes, function names.
Why answers beat result lists
Both approaches return documents. An assistant built on retrieval composes the answer and cites the page, which removes the step where the reader has to open four results and skim for the relevant paragraph.
Use both where you can
Semantic retrieval for natural-language questions, exact matching for identifiers. Treating them as competitors rather than complements produces a system that fails confidently in one direction.
Common questions
- Should I replace my site search?
- Not necessarily replace — supplement. Keep exact matching for identifiers and add semantic answering for questions, which is where the existing search is failing.
- Does AI search work on a small site?
- Yes, though the benefit scales with content. On a small site the more valuable output is often the gap report telling you what to write.
From the blog
All posts- EvaluatingChatbase alternatives for teams that need cited answers (2026)Matter Chat, Chatbase, Intercom Fin, Tidio Lyro, DocsBot and Chatling compared on citations, refusal behaviour, handoff and what the entry price meters. Checked on the vendors' own pages, with the test to run on all six.Read
- EvaluatingHow to test an AI support tool before you trust itEvery AI support tool demos well, because demos ask questions the content covers. Four questions that separate them, and what a good answer looks like.Read
- EvaluatingThe demo is a bad predictor of AI support qualityA polished demo tells you the product can answer questions it was prepared for. The useful evaluation happens at the edges — outside the script, on your content.Read
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