AI search isn't one thing. ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews each handle your query differently. But the core process they share is the same: read the question, search the web, pick the good sources, write a grounded answer with citations.
Here's what happens at each step, why the engines diverge, and what it means if you want your brand to get cited.
AI Search vs. Traditional Search: The Real Difference
Traditional Google ranks ten links and you click. AI search reads your question, pulls from many sources at once, and writes one answer with a few cited URLs underneath.
The behavior shift on the user side is just as big. AI search queries are dramatically longer than traditional Google queries. People type to AI the way they'd ask a colleague. Full sentences, context, follow-ups. That single change reshapes what the engine has to do under the hood.
One quick disambiguation. If you searched "how does AI search work" and landed on Microsoft or Cloudflare documentation, you saw something different. Those pages describe Azure AI Search or Cloudflare AI Search, which are vector database products that companies use to add semantic search inside their own apps. Important tech, totally different topic. This article is about the consumer-facing AI search engines that answer questions for end users: ChatGPT search, Perplexity, Google AI Overviews, Gemini, Claude, and Bing Copilot.
The 4 Steps Every AI Search Engine Runs
Almost every engine in this space runs the same four-step loop. The implementations vary. The shape doesn't.
Step 1: Query understanding. The model reads your question and figures out intent, then expands it. Synonyms, related concepts, and what Ahrefs calls query fan-out: breaking a complex question into smaller parallel sub-queries. Ask for "a 5-day trip to Japan" and the engine simultaneously searches for hotels in Tokyo, weather in Kyoto, train passes, and a dozen other angles you didn't type.
Step 2: Retrieval. The engine fires those sub-queries against a search index. Sometimes that's a partner index like Bing (in ChatGPT's case). Sometimes it's a SERP API. Sometimes it's a proprietary cached crawl. The output is a candidate pool of 10 to 50 documents per sub-query.
Step 3: Source evaluation. This is where most explainers gloss over. The system ranks candidates by authority, recency, relevance, and cross-source agreement. A claim that shows up in three independent sources beats one that appears in one. A page from a domain it has cited successfully before beats a brand-new domain on the same topic.
Step 4: Generation with citation. The model takes the surviving sources, fills its context window with their text, and generates an answer grounded in that text. The links it shows below the answer are the receipts. This whole pattern has a name: Retrieval-Augmented Generation, or RAG.
How ChatGPT Search Works (Bing-Backed)
ChatGPT's web search runs on Bing's index. When you trigger a search inside ChatGPT, the model issues queries against Bing, pulls back results, evaluates them, and writes an answer.
OpenAI also runs its own crawlers. OAI-SearchBot is the one that fetches pages live to answer queries in real time. GPTBot crawls pages for training data, which is a separate use case (newer ChatGPT models will know what was on your site at training time, but live answers are powered by OAI-SearchBot). The bot user agents matter because if your site blocks either of these in robots.txt or via a WAF rule, you're invisible to that engine.
The practical implication: ChatGPT visibility is heavily downstream of Bing. Submit your sitemap to Bing Webmaster Tools, resolve coverage issues there, and you've covered the largest input ChatGPT uses to find your content.
How Perplexity Works (Sonar and Context-Window Saturation)
Perplexity calls itself an answer engine, not a search engine, and the architecture reflects that distinction. Its core retrieval engine is named Sonar and is built on Meta's Llama 3.3 model, per Perplexity's Wikipedia entry. Perplexity Pro lets users swap in backend models from OpenAI, Anthropic, and Google.
What sets Perplexity apart is how aggressively it grounds answers in retrieved text. The system pulls a wide candidate pool, ranks sources by authority and freshness, then loads as much source material as the model's context window allows before generation. The logic is straightforward: when the model is reading real source text, it has less room to invent things. Less creativity, more accuracy.
Perplexity also leans hard on Reddit. Profound's citation tracking shows Reddit is the #1 most-cited domain in Perplexity answers, ahead of every other source. If your category has active Reddit discussions and your brand isn't in them, you're handing competitors a free lane.
How Google AI Overviews Work
AI Overviews are the boxed answers that appear at the top of Google search results for a growing share of queries. They run on Gemini and pull from Google's main index, not from a separate AI-specific crawl.
That last detail is what makes them different from every other engine in this list. AI Overviews source most of their citations from pages Google already considers high-quality enough to rank organically. Independent studies of cited URLs have generally found heavy overlap with the organic top 10 to 12, which means classic SEO fundamentals (helpful content, sound technical setup, real authority signals) still pull most of the weight.
Google's separate AI Mode (the standalone conversational interface) behaves more like ChatGPT and Perplexity. Onely's analysis of AI Mode citations found only about 14% of cited URLs ranked in the visible Google top 10, so the citation pool is wider than what classic SERPs reveal.
The key bot to know here is Google-Extended. It's the user agent token Google uses to govern whether your content is eligible for Gemini and AI Overview training. Block it in robots.txt and you opt out.
How Claude and Gemini Handle Search
Claude added live web search roughly six months after ChatGPT, and the implementation is closer to ChatGPT's pattern: pull from a search backend, evaluate, generate with citations. The crawler to know is ClaudeBot (live search) and anthropic-ai (training data). Same two-bot split as OpenAI.
Gemini grounds answers through Google Search when the model decides external data is needed. Inside the Gemini chat app, that grounding is what powers the "Show sources" feature. If you're optimizing for Gemini, you're effectively optimizing for the Google index it grounds against.
Bing Copilot is the simplest of the bunch. It's a thin layer on top of Bing's regular search results, with answer generation by GPT-4. If you're indexed and ranking in Bing, you're a candidate for citation in Copilot.
What This Means If You Want To Get Cited
Three things matter, and they're all downstream of the four-step loop above.
Be reachable. Most "we're not showing up in AI" problems are simpler than people think. The bot can't reach the page. A robots.txt rule blocks GPTBot. A Cloudflare or DataDome rule rate-limits OAI-SearchBot. A JS-only render delivers a blank shell to a crawler that doesn't execute JavaScript. If you don't verify reachability, every other tactic is wasted effort. This is the gap GeoToolbox was built to surface, with a free 11-bot reachability probe.
Be a clear answer to a clear question. AI search rewards content that opens with a direct answer in the first paragraph, supports it with structured data, and presents claims in a format the model can lift (short paragraphs, tables, bulleted definitions). If your top-of-page is brand storytelling and the answer is buried in section four, you'll lose to a competitor who put the answer up top.
Build authority signals across sources. RAG evaluation rewards cross-source agreement. A claim that appears on your site, in a Reddit thread, in a YouTube transcript, and in a third-party listicle is far more likely to surface than the same claim on your site alone. Treat off-site presence (Reddit, Quora, podcast transcripts, expert directories) as part of your visibility stack, not a separate channel.
Frequently Asked Questions
Is AI search the same as Azure AI Search?
No. Azure AI Search and similar products (Cloudflare AI Search, Elasticsearch with vector extensions) are infrastructure tools that companies use to add semantic search inside their own applications. AI search engines like ChatGPT, Perplexity, and Google AI Overviews are user-facing products that answer questions from the open web. They share underlying techniques (embeddings, RAG, semantic matching) but solve different problems.
Does traditional SEO still matter for AI search?
Yes. Google AI Overviews source most citations from pages that already rank well in the underlying Google search results. ChatGPT runs on Bing's index, so being well-indexed in Bing is a direct lever. Even Perplexity (which uses its own retrieval) factors in authority signals that overlap with classic SEO: backlinks, domain credibility, content quality. SEO isn't sufficient on its own anymore, but it's still necessary.
Do AI search engines crawl pages live or rely on a cached index?
It depends on the engine and the action. Live search queries (ChatGPT search, Perplexity, Claude with web search) hit a search index or SERP API in real time when you ask a question. Training data crawls (GPTBot, anthropic-ai, Google-Extended) happen on a separate schedule and feed the model weights. If you want to control either, robots.txt and your WAF are the levers. A page can be reachable to one and blocked to the other.
Why do AI engines cite Reddit so much?
Because Reddit is dense with people explaining things to each other in plain language, with cross-validation from other commenters. That format is almost ideal for RAG: short, declarative, cross-referenced, often with concrete examples. Reddit is the #1 most-cited domain in Perplexity and ranks in the top three across ChatGPT, Grok, and Google AI Overviews per Profound's tracking. For brands, the implication is that being present in active Reddit threads (helpfully, not promotionally) is a high-leverage citation play.
Where to Go from Here
The four-step loop is the model. The engine-specific differences are the details. The reason any of this matters to you is that step 2 (retrieval) can fail silently. If GPTBot or OAI-SearchBot or ClaudeBot can't reach your page, none of the rest of the loop runs.
If you're not sure your site is reachable to the bots that matter, run a free bot reachability audit with GeoToolbox and see which of the eleven major AI crawlers can actually fetch your pages today. It's the cheapest part of the AI visibility stack to verify, and it's the one most brands skip.