AI search engine

How does an AI search engine work?#

An AI search engine works in five steps: it decides whether a question needs the web, rewrites it into several searches, retrieves candidate pages from an index, reranks passages from those pages, and has a large language model write an answer citing the passages it used.

1. 1

   Decide whether to search

   Stable facts come from the model’s training; current, specific or comparison questions trigger retrieval. ChatGPT searched on 34.5% of prompts in February 2026 (Semrush) and Claude on 36.6% (Profound).

2. 2

   Fan the question out

   The engine writes narrower sub-queries — query fan-out. ChatGPT averaged about 7.6 per prompt after August 2026 (Nectiv); Deep Search in Google’s AI Mode can run hundreds.

   Your lever: Cover the follow-up questions a buyer would check, each with a clearly titled page or section.

3. 3

   Retrieve candidates from an index

   Each engine searches its own source: Google’s index, Bing plus OpenAI’s own, Brave, or Perplexity’s.

   Your lever: Be crawlable by that engine’s search bot and indexed where it looks.

4. 4

   Rerank passages

   Pages are split into chunks and a more precise model re-scores them against the query — reranking. Perplexity documents this step: cross-encoders score “self-contained spans.”

   Your lever: Answer each section’s question in its first sentence.

5. 5

   Generate and cite

   A large language model writes the answer from the top passages and links the ones it relied on — the step called grounding.

What are the main AI search engines?#

The main AI search engines in 2026 are ChatGPT search, Google AI Overviews and AI Mode, the Gemini app, Perplexity and Claude — each retrieving from a different index, which is why the same question can cite different sources on each.

Microsoft Copilot is reported in Bing Webmaster Tools’ AI Performance report, alongside Bing — the same Bing indexing that feeds ChatGPT’s providers. Because the indexes differ, overlap between engines is low: Claude and ChatGPT shared only 8% of cited domains in Profound’s 2026 study. Being cited by one says little about the others.

AI search engine vs traditional search engine: what's the difference?#

A traditional search engine returns a ranked list of links for the user to open, while an AI search engine reads those pages itself and returns a written answer with a few citations — so the competition shifts from ranking a page to being quoted in a passage.

The two are converging. Google puts AI Overviews above classic results and, since I/O 2026, runs AI Overviews and AI Mode as “one seamless Search experience.” The cost of the shift shows in the clicks: Pew found users clicked a classic result on 8% of visits when an AI summary appeared, against 15% without. See zero-click search.

How do you get cited by an AI search engine?#

Getting cited by an AI search engine takes four things: its search crawler can fetch your pages, the content is in plain HTML, the page is indexed where that engine looks, and each section answers one sub-question in its first sentence — backed by mentions on sites engines already trust.

Search crawler allowedRequired

OAI-SearchBot, Claude-SearchBot, PerplexityBot and Googlebot, in robots.txt and at the CDN. Training bots are a separate decision. See AI crawlers.

Content in the initial HTMLRequired

Only Google runs your JavaScript; OpenAI’s, Anthropic’s and Perplexity’s fetchers read raw HTML. See server-side rendering.

Indexed where the engine looksRequired

Google for AI Overviews, AI Mode and Gemini; Bing for ChatGPT’s providers; Brave for Claude; Perplexity’s own crawler for Perplexity.

Answer-first passagesHelps

Engines rerank passages, not pages. See answer-first content.

Visible freshnessHelps

Claude adds the current year to 94% of its sub-queries, and Perplexity filters stale pages out before ranking.

llms.txt or AI-specific markupNo effect

No major engine has confirmed using llms.txt, and Google says its AI features need no special files or schema.

Related terms#

• RAGHow LLMs answerRetrieval-augmented generationA technique in which an AI system first retrieves relevant documents from an index and then gives them to a large language model to write its answer, so the response can cite current sources instead of relying only on what the model memorized in training.Read the entry
• AI search & GEOAI OverviewsAI-generated summaries shown at the top of some Google search results, written by a Gemini model from pages in Google’s search index and linked to their sources — so they are won through ordinary Google eligibility, not a separate submission or special markup.Read the entry
• GEOAI search & GEOGenerative engine optimizationThe practice of making content easy for AI answer engines such as ChatGPT, Perplexity and Google AI Overviews to retrieve, quote and cite — where SEO competes for a ranked link, GEO competes to be a named source inside the generated answer.Read the entry
• How LLMs answerQuery fan-outAn AI search technique in which the engine rewrites one user question into several narrower sub-queries, runs them in parallel and builds its answer from the combined results — which is why a page can be cited for a prompt it doesn’t rank for.Read the entry
• AI crawlersAI crawlersAutomated bots run by AI companies that fetch web pages for one of three jobs — training models, building an AI search index, or retrieving a page live for a user’s question — and because each job uses its own user agent, each can be allowed or blocked separately.Read the entry
• AI search & GEOAI citationA link or source reference that an AI answer engine attaches to its response to show where a claim came from, and it differs from a brand mention in that it points to a specific page that can be clicked and can send traffic.Read the entry