How AI Search Works: From Semantic Indexing to RAG

Type a question into a search bar and get an actual answer instead of a wall of blue links. That feels like magic, but it’s four distinct steps working in sequence: 1crawling, 2semantic indexing, 3retrieval, and 4generation. Understanding them makes it obvious why AI search finds what keyword search misses (see our companion piece, AI Search vs. Traditional Search: What’s the Difference?).

Before anything can be searched, it has to be found. A crawler works through a site page by page, following links the way a visitor would, and keeps a record of what exists and where it lives. This part isn’t unique to AI search — it’s the same discovery step every search engine depends on. What’s unique is what happens to that content next.

Traditional site search stores words. Semantic indexing stores meaning. Each page is broken into smaller chunks, and every chunk is converted into an embedding — a list of numbers that represents what that chunk is about rather than which words it contains.

This is the step that makes paraphrasing, synonyms, and even typos a non-issue. A visitor searching “do you ship internationally” can match a page titled “Where we deliver” even though the two sentences barely share a word — a real search a keyword index would miss entirely. Meaning transfers across languages too, which is why the same index can answer a question asked in a different language than the one the content was written in.

Chunking is a judgment call, not a formality. Too large a chunk buries the relevant sentence among unrelated ones; too small a chunk loses the surrounding context needed to understand it. Good indexing chunks around natural boundaries — headings, paragraphs, FAQ entries — rather than an arbitrary line count.

When a visitor types a question, the question itself gets embedded the same way the content was. The search compares that embedding against every stored chunk and pulls out the ones that sit closest to it, ranked by how relevant they are, not by how many words match. This is the step that actually answers “does this page cover what the visitor is asking,” rather than “does this page contain these words.”

Relevance isn’t only about closeness in meaning either. A well-built system can also weigh recency or let a site owner curate which result should win for a given query, on top of the semantic match.

Retrieval on its own would already be more useful than keyword search, handing back a ranked list of matching chunks. Retrieval-augmented generation, RAG, takes it one step further: the retrieved chunks are handed to a language model along with the visitor’s original question, and the model composes an actual answer.

The important word there is retrieved. The model is deliberately restricted to answering from what was actually found on the site, not from whatever it already knows about the world. That grounding is what keeps the answer accurate to the site’s own content and lets it point back to the page the answer came from, instead of inventing something that merely sounds right.

Why the order matters

Each step only works if the one before it did its job. A page the crawler missed can’t be indexed. A chunk boundary that splits a sentence in two damages its meaning. An index that only captures keywords can’t be searched semantically. And generation is only ever as good as what retrieval handed it — a language model shouldn’t, and in a well-grounded system can’t, invent an answer that was never in the retrieved chunks. Most of what looks like “AI search getting things wrong” traces back to one of the earlier steps, not the last one.

Where this approach has limits

RAG is only as current as the last crawl. If a price or policy changes on the site and the index hasn’t been refreshed yet, the answer will reflect the old version until the next crawl runs. That’s why automatic re-indexing on content changes matters just as much as retrieval and generation themselves — a search feature is only as trustworthy as the freshness of what it’s searching.

What this means for visitors

The practical result is a search box a visitor can talk to in their own words. They don’t need to guess the exact phrase used on the page, and they don’t need to read three results to find the one that actually answers their question. This is the pipeline behind Breezy AI Search: crawling, semantic indexing, retrieval, and RAG, applied to a customer’s own site content and nothing else.