When someone types a question into ChatGPT, Perplexity, Gemini, or Google’s AI Mode, an answer appears in seconds, often with links to sources. Behind that answer is a multi-stage process that decides which websites get used and cited. Understanding this process is the foundation of any AI visibility strategy, because every stage creates an opportunity to be included or a reason to be left out.
This guide walks through how AI search works, from the moment a query is entered to the moment a cited answer appears. For a broader introduction, see our pillar guide on how AI search works.
The Five Stages at a Glance
- Understanding the query: interpreting what the user actually wants
- Retrieving candidates: finding potentially relevant documents
- Selecting passages: choosing the most useful, trustworthy information
- Generating the answer: writing a response from selected information
- Attributing sources: showing citations and links
Different AI search products implement these stages differently, but the overall pattern is similar across most systems.
Stage 1: Understanding the Query
The process starts with interpretation. The AI analyses the user’s words, the conversation history, and sometimes context such as location. It works out the intent: is the user looking for a definition, a recommendation, a comparison, instructions, or current news?
The system also decides whether it needs to search at all. Some questions can be answered from the model’s trained knowledge. Others, especially those involving current events, prices, local information, or specific products, trigger a web search.
Query Rewriting and Fan-Out
Complex questions are often rewritten into clearer search queries or split into several sub-queries. Google has described a “query fan-out” technique in AI Mode, where multiple related searches run at once to gather broader information. This means your content can be retrieved for sub-questions, not only the user’s original wording.
What this means for you: Cover topics comprehensively, including related sub-questions, so your pages match more of the searches generated behind the scenes.
Stage 2: Retrieving Candidates
Next, the system searches an index for documents that may be relevant. This index might be a traditional search index, such as Google’s, a platform’s own index built by its crawler, or a combination of sources. Retrieval often uses both keyword matching and semantic matching, which compares meaning rather than exact words.
Only content that has been crawled and indexed can be retrieved. If crawlers are blocked or your content is hidden behind scripts or logins, it is invisible at this stage.
What this means for you: Make sure relevant crawlers can access your site, content renders in HTML, and pages are indexed. An AI SEO audit can confirm this.
Stage 3: Selecting Passages
From the retrieved candidates, the system identifies the passages most useful for answering the question. It typically favours content that is:
- Relevant to the exact question and intent
- Clear, with direct answers that are easy to extract
- Specific, containing facts, numbers, steps, or definitions
- Trustworthy, from credible sources with evident expertise
- Current, especially for time-sensitive topics
This is where many well-ranking pages lose out. A page might be relevant but bury its answer in long paragraphs. Another page with a crisp, factual passage may be chosen instead.
What this means for you: Structure content with descriptive headings and answer-first sections. Include evidence and demonstrate expertise. Our guide to AI SEO ranking factors explains these signals in more depth.
Stage 4: Generating the Answer
The language model now writes a response using the selected passages as grounding material. This technique is known as retrieval-augmented generation, or RAG. The model synthesises information from several sources, rephrases it, organises it logically, and adapts it to the user’s question.
Because the model combines sources, your content may contribute a single fact, a definition, a step, or a recommendation rather than the whole answer.
What this means for you: Make each section self-contained and precise, so any part of your page can be used accurately on its own.
Stage 5: Attributing Sources
Finally, many AI search products show citations. These can appear as numbered references, inline links, source cards, or a list of sources beside the answer. Citations let users verify information and visit the original pages. Some systems cite every source used; others show only a selection.
What this means for you: Being cited brings visibility, credibility, and referral traffic. Track citations in your target prompts and learn from the sources that win. Read more in our guide to AI search citations.
How This Differs From Traditional Search
Traditional search ranks whole pages and shows a list of links. AI search works at the passage level and produces a single synthesised answer. In traditional search, you compete for a position on a page. In AI search, you compete to be part of the answer itself. Many signals overlap, but clarity, specificity, and trust carry extra weight. Our comparison of AI SEO vs traditional SEO covers this shift in detail.
Putting It Into Practice
- Allow AI search crawlers and keep content crawlable
- Target clear intents and cover related sub-questions
- Lead with direct answers under descriptive headings
- Add evidence, examples, and expert authorship
- Keep facts current and brand information consistent
- Test priority prompts and track citations over time
Final Thoughts
AI search may feel like a black box, but its stages are logical. Be accessible so you can be retrieved, be clear so you can be selected, be trustworthy so you can be used, and be specific so you can be cited. Content that meets these standards performs well across ChatGPT, Perplexity, Gemini, and Google’s AI features. For foundational context, start with what AI search is.
Frequently Asked Questions
How does AI search work in simple terms?
AI search interprets a question, retrieves relevant web content, selects the most useful passages, writes an answer using them, and often shows citations linking to the original sources.
What is retrieval-augmented generation?
Retrieval-augmented generation, or RAG, is when an AI model retrieves relevant documents first, then uses them to generate its answer. It improves accuracy and allows the system to cite sources.
Does AI search use Google’s index?
It depends on the product. Google’s AI features use Google’s index, while ChatGPT, Perplexity, and others use their own crawlers, indexes, and partner search data to retrieve web content.
Why was my page not cited even though it ranks well?
Ranking helps retrieval, but selection favours clear, specific, trustworthy passages. If your answer is buried, vague, or outdated, the AI may choose a clearer passage from another source instead.
What is query fan-out?
Query fan-out is when an AI system splits a complex question into several related searches. It gathers broader information, meaning your content can be retrieved for sub-questions, not just the main query.
How can I improve my chances of being cited?
Ensure crawler access, target clear intents, lead sections with direct answers, add evidence and expertise, cover related questions, and keep information current and consistent across your website.