How does AI search work and how is it different from Google?
How AI search works, from query fan-out and retrieval to a cited answer, how it differs from Google's ranked results, and how to measure your place in it.
Google's classic model is simple to describe. You type a query, the engine looks up pages in an index, ranks them, and shows you a list of links. AI search changes the last step. Instead of a list, you get a written answer, usually with a handful of cited sources. Behind that answer is a longer chain of steps than most people realize, and each step is a place where a website can be included or dropped.
This guide explains how AI search works in plain terms, how it differs from Google, what stays the same, and how to see where your site stands.
The pipeline behind an AI answer
Different products vary in the details, and vendors change them often. The overall shape is consistent enough to be useful.
1. The question is interpreted. The engine works out what you are asking, including context from the conversation. Long, conversational prompts replace short keyword queries.
2. The query is expanded, often called query fan-out. Rather than searching your exact words, many AI search products break the question into several sub-queries and run them. A prompt like "best CRM for a ten-person agency" might trigger searches for CRM comparisons, agency CRM features, pricing, and reviews. This is why you can be missing from an answer even if you rank for the obvious phrase: you may not match the sub-queries.
3. Sources are retrieved. The engine fetches candidate pages from a search index, from its own crawler data, or from a partner's index. Retrieval-augmented generation is the term for this pattern: the model looks things up rather than answering from memory alone.
4. The model reads and synthesizes. It pulls relevant passages from retrieved pages and writes an answer. Pages that state facts clearly, in text the crawler can read, are easier to use.
5. Sources are cited. Some of what was fetched is shown as citations. Fetched is not the same as cited: an engine can read ten pages and show three.
6. The answer varies. Run the same prompt twice and you may get different wording and different sources. That is normal.
How this differs from Google's classic results
| Aspect | Classic Google results | AI search answers |
|---|---|---|
| Output | Ranked list of links | Written answer with cited sources |
| Query style | Short keywords | Conversational prompts, often followed by follow-ups |
| Matching | Your query against an index | Sub-queries from a fan-out, then synthesis |
| Success | Position and click-through | Being named and cited, with fewer clicks |
| Stability | Positions move slowly | Answers can change between runs |
| Visibility data | Search Console | Prompt tracking and citation analysis |
Two things stay the same. The engines still need to find and read your pages, so crawlability and clear content matter. And quality signals still help, because the retrieval step tends to favor pages that are already well established. Google's own guidance for site owners says the usual search fundamentals apply to its AI features.
What this means for a website
- You compete for inclusion, not position. An answer names a few brands. Most are not in it.
- Third-party sources matter more. Engines often cite review sites, forums and news rather than the brand's own pages.
- Crawler access is a gate. Different bots serve different purposes: some fetch pages to answer a live question, some collect data for training, and some power a search index. Blocking the wrong ones can remove you from answers.
- Measurement changes. Rankings do not describe your visibility. You need to track prompts and citations.
Seeing it for yourself
You can learn a lot with free steps. Run a handful of buyer questions in ChatGPT, Gemini and Perplexity. Note which brands appear and which pages are cited. Then check whether the cited pages say things your pages do not.
A dedicated platform makes this systematic, and the stages above map onto specific features.
Promptwatch: a view of each stage
Promptwatch is the platform that follows an answer from fan-out to click:
- Fan-out: it shows query fan-outs per model, the sub-queries an engine ran. A free GEO Inspector Chrome extension shows ChatGPT fan-outs and which sources were fetched versus cited, running locally in your browser.
- Retrieval and citation: citation analytics at page and domain level, Page Tracker for specific URLs, and Citation Trends that classify every citation by content type and source type.
- Access: Agent Analytics logs AI crawlers in real time, IP-verified, with real bot names such as GPTBot, OAI-SearchBot, ChatGPT-User, ClaudeBot and PerplexityBot, and shows a citation rate per page.
- Outcome: visitor analytics attribute AI-referred traffic and conversions.
- Action: content gap analysis and the Action Board point to what to publish or fix.
Pricing: Explore is free (10 prompts, ChatGPT only), Essential is $95 a month, Professional $245 with crawler logs, and Business $579. Agency plans are $199, $399 and $799.
Other tools cover parts of this. AthenaHQ tracks visibility and turns gaps into tasks. Scrunch AI adds site audits and bot traffic reporting. Profound is an enterprise suite. Ahrefs Brand Radar reports on AI crawler activity and several answer engines. Only some of them show the crawl-to-citation path or tie AI visits to conversions.
What AI search cannot be made to do
You cannot instruct an engine to recommend you, and no vendor can promise it. The inputs you control are access, clear and sourced content, presence on the sources engines trust, and how well your pages answer the sub-questions. The output varies by model and by day.
A short first project
Pick ten buyer questions. Run them across several engines. List the sources cited and the brands named. Check whether crawlers can reach your key pages. Fix the biggest gaps, then repeat in a month and compare. That loop is how AI search stops being a mystery and becomes a channel you can manage.