↗How it works
How AI search works — from question to cited answer
You don't need to be a machine-learning engineer to make good decisions about AI search. You do need a working model of what happens between someone typing a question and an answer appearing. This is that model, in plain English.
By Prithvi Somavarapu · Updated · 3 min read
The four steps behind most AI answers
Different products implement this differently, but the broad pattern is shared by Google’s AI features, ChatGPT search, Perplexity, Gemini and Copilot.
1. Understand and rewrite the question
The system first works out what is being asked. Long, conversational questions are often rewritten into one or more shorter searches. Google describes a technique it calls query fan-out for AI Overviews and AI Mode: issuing multiple related searches across subtopics and data sources to build a response (Google Search Central). OpenAI’s description of ChatGPT search similarly refers to sending rewritten queries to search providers.
What this means for you: your page doesn’t only compete for the exact question typed. It competes for the sub-questions the system generates — “pricing”, “integrations”, “alternatives”, “reviews”. Content that answers those sub-questions directly has more opportunities to be retrieved.
2. Retrieve candidate sources
Next, the system fetches documents. Where they come from depends on the platform:
| Platform | Primary retrieval source (per public documentation) |
|---|---|
| Google AI Overviews & AI Mode | Google’s search index |
| Gemini | Can ground answers using Google Search |
| ChatGPT search | Third-party search providers, OpenAI’s own crawler (OAI-SearchBot) and partner content |
| Perplexity | Its own index (PerplexityBot) plus real-time fetches |
| Microsoft Copilot | Bing’s web index |
What this means for you: if a platform’s crawler or index can’t access your pages, they can’t be retrieved there. This is why technical SEO and a deliberate crawler policy matter for AI visibility.
3. Generate an answer grounded in the sources
A large language model reads the retrieved material and writes an answer. This approach is often called retrieval-augmented generation (RAG): the model’s general language ability is combined with specific, fetched evidence. The model tends to use passages that directly address the question, state facts clearly and agree with other sources.
What this means for you: vague marketing copy is hard to use. A sentence like “Acme Payroll supports automated TDS calculation for Indian companies, with filings generated each quarter” can be used; “we deliver innovative payroll excellence” cannot. This is the core of answer engine optimization.
4. Attach citations
Finally, the product decides which sources to show as citations and how. Perplexity numbers its sources inline; Google shows supporting links within and beside AI Overviews; ChatGPT shows source links and a sources panel when it searches; Copilot shows linked references. Not every source used is shown, and not every answer includes citations.
What this means for you: being used and being visibly credited are related but not identical. Measure both.
Where brand mentions come from
Notice that your brand can appear in an answer even if none of your pages were retrieved. If the retrieved sources are a review site, a comparison article and a news story that all mention you, the answer may mention you too — and describe you the way they do. That is why GEO, brand mention building and entity optimization work on sources beyond your own website.
There is also the model’s background knowledge from training. For well-known brands, a model may “know” things about you even without retrieval — including outdated facts. Consistent, current information across the web is the best long-term correction.
What this model tells you to do
- Make sure you can be retrieved. Crawlable, indexable, fast pages; no accidental blocks of search crawlers.
- Answer the sub-questions. Cover pricing, comparisons, use cases, limitations and how-tos clearly — not just your headline keyword.
- Write usable passages. Specific, self-contained, factual sentences near the top of each section.
- Be described well elsewhere. Reviews, directories, comparisons and coverage shape mentions and recommendations.
- Keep facts consistent. Name, description, offering, pricing model and locations should match everywhere.
Limits of this model
This is a simplification. Platforms use different models, rerankers, safety filters and presentation rules, and they change frequently. None publish a full description of how sources are selected. Treat any claim of a precise “AI ranking factor” with caution, and judge tactics by whether they also make your content more useful to people.
FAQ
How AI search works: frequently asked questions
Do AI chatbots search the web every time?
What is the difference between training data and retrieval?
Why do two people get different answers to the same question?
Does blocking AI crawlers stop my brand appearing in AI answers?
Related to How AI search works
- Glossary Retrieval-Augmented Generation (RAG) An AI technique where the system first retrieves relevant documents, then generates an answer based on them. Read
- Glossary Large Language Models (LLMs) AI models trained on very large amounts of text to understand and generate language — the technology behind ChatGPT, Gemini and Copilot. Read
- Guide AI crawlers and robots.txt: which bots do what, and what to allow OAI-SearchBot, GPTBot, ChatGPT-User, PerplexityBot, Google-Extended and more — what each AI crawler does, and how to set robots.txt rules deliberately. Read
- Platform AI search platforms How ChatGPT, Google AI Overviews, AI Mode, Perplexity, Gemini and Microsoft Copilot find and cite sources — and how to improve your brand's visibility in each. Read
- Comparison ChatGPT vs Perplexity ChatGPT search and Perplexity compared from a brand's perspective — source discovery, crawlers, citations, measurement and what to optimise on each. Read
- Service Technical SEO Technical SEO audits and fixes: crawlability, rendering, indexation, site architecture, Core Web Vitals, structured data, migrations and AI crawler access. Read
AI Visibility Audit
Is your brand visible where your customers are searching?
Our AI Visibility Audit shows how you appear across Google and AI answer engines today — and what to fix first.
Prefer email? prithvi@prisol.org