How AI Search Algorithms Work: Google, ChatGPT & LLMs Explained

I just finished watching the videos from Google’s 2026 I/O Summit and my big takeaway is the shift toward AI conversational search using your voice with new features like AskMaps, AskYouTube and Docs Live that makes Gemini more of a thinking partner.

The future of search is going to be natural conversations with AI and there is going a lot less time spent typing and more time speaking directly with AI as a personalized virtual assistant that can pull from all of the data that we choose to give it access to online.

If you want to get a feel for how this is going to work and how it’s going to change how people find information and expertise to solve their problems, I highly recommend giving Google’s new Gemini Spark 24/7 personal AI agent a try when it becomes available.

For Google search, they announced they are reimagining search with their new Intelligent Search Box, which goes beyond autocomplete by helping you formulate your questions as you ask with AI suggestions. They are also putting AI mode, agentic search agents and voice dictation much more prominent in what they’re called the biggest change in search in their 25 years of existence.

people used to type a question and get ten blue links but the new reality is you get an initial paragraph that can be expanded on Google’s AI Overviews, which means you need to it cite your expertise because you now get a lot less clicks to your website.

For AI searches on ChatGPT, it tends to recommend a specific product, a specific person, or a specific company rather than giving a series of options to choose from.

In this guide, I want to walk you through how AI search algorithms analyze and cite your expertise. I’ll try not to overwhelm you with technical jargon, but because understanding what’s actually happening under the hood of these LLM models will change how you think about your presence online.

The Old SEO Game Vs. The New AI SEO

For about twenty years, SEO worked on a simple premise: you had a website that Google’s crawler visited, analyzed, figured out what it was about, and matched it to search queries.

The PageRank algorithm that built Google’s trillion dollar search empire gave you a ranking for search queries based on relevancy and the number of authoritative sites that linked to your website. It was a popularity contest with some on-page optimization layered on top.

That model of SEO isn’t dead but it’s no longer the whole story.

AI search doesn’t work by matching your page to a query and ranking it. It works by reading thousands of pages, synthesizing what they say, and generating a single confident answer. The source gets a citation and everything else disappears.

That shift from ranking to citation is the fundamental change most people haven’t fully absorbed yet.

Understanding Retrieval Augmented Generation (RAG)

When you hear people talk about how ChatGPT or Perplexity “works,” the term that comes up is RAG: Retrieval-Augmented Generation.

It sounds complicated but the concept is actually pretty intuitive.

Imagine you’re a researcher preparing a briefing. You search through dozens of documents, pull out the most relevant passages, and write a summary for your client. You cite your sources at the bottom. That’s essentially what a RAG system does, but at machine speed across the entire web.

There are three stages to this pipeline.

1. Retrieval

The system goes out and finds candidate pages. It’s using a search index, crawlers, and something called vector embeddings to identify text that’s mathematically similar in meaning to the query. Not just matching keywords. Matching intent.

2. Re-Ranking

Not everything retrieved is equally useful. The system applies a scoring filter to decide which pages best answer the question. This is where factors like how the content is structured, how recent it is, and how trusted the source is come into play.

3. Generation

The model synthesizes those retrieved passages into a response. It doesn’t quote them verbatim. It reads them and writes something new. The cited sources are the ones whose content shaped the answer most directly.

If your content fails at stage one (it wasn’t retrieved) or stage two (it wasn’t ranked high enough in the re-ranking layer), you don’t exist in the AI answers.

The 6 Biggest LLM Platforms And How They Are Different

While there are a lot of different LLM platforms crawling the web today, let’s just focus on the biggest ones that your customers are actively using every day.

Right now, the most popular LLMs for search are Google AI Overviews, Google AI Mode, Gemini, ChatGPT, Perplexity, and Claude.

They all use AI to generate answers but they pull from different places and weigh things differently. Treating them as one channel is the most common mistake I see.

1. Google AI Overviews

Google AI Overviews run on Gemini and sit on top of Google’s existing organic index. If you already rank in the top ten for a query, you have a real chance of being cited in the AI Overview for that same query.

An Ahrefs study of 1.9 million citations found that 76% of pages cited in AI Overviews ranked in the top ten organic results though more recent data from early 2026 puts that overlap closer to 38%, suggesting AI Overviews are now pulling from a much broader pool than before.

Either way, ranking in the top ten no longer guarantees citation. Google is making editorial choices inside that pool. It’s selecting for pages that directly answer the question, that are structured clearly, and that carry strong expertise signals. A page that ranks number three but buries the answer in paragraph nine may lose the citation to a page at position seven that leads with a clean, direct response.

2. Google AI Mode

Google AI Mode is a different beast, and it’s easy to confuse it with AI Overviews. AI Overviews are a box that appears above your standard search results. AI Mode is a separate, fully conversational interface users actively select, more like ChatGPT than a search results page. There are no ten blue links underneath. You either get cited or you don’t appear at all.

The mechanics are also different. According to Google, AI Mode uses a fan-out technique that breaks a query into subtopics and issues a multitude of searches simultaneously on the user’s behalf. That means it can pull from a wider range of sources on a single question, which creates both opportunity and risk: you might get cited on a sub-question you didn’t even know was relevant to your topic.

3. Gemini

Gemini as a standalone app is its own channel, separate from both AI Overviews and AI Mode. According to Google’s CEO Sundar Pichai’s Keynote at the 2026 I/O Summit, the Gemini app has surpassed 900 million monthly active users, doubling from a year ago and daily requests have grown over 700%.

The important distinction for content creators is that Gemini and AI Overviews use different citation pathways. Ranking well for AI Overviews does not automatically get you cited in Gemini, and the reverse is also true. They share the same underlying model but draw from different retrieval signals.

Where Gemini stands out is E-E-A-T weighting. Because Google controls both the quality rating framework and the model that consumes it, Gemini applies E-E-A-T signals more heavily than any other AI platform. This means experience and authoritativeness aren’t soft signals here, they’re baked into how the model evaluates whether to trust what you’ve written.

For anyone producing content in a health, finance, or professional services space, that makes Gemini the platform where author credentials and sourcing discipline matter most. Without providing it with strong signals about your expertise and credentials, you may see a drop in both traffic and citations.

4. ChatGPT

ChatGPT pulls from two sources: its training data, which has a cutoff and gets stale, and live web retrieval through SearchGPT, which launched fully in early 2025. The live search layer runs on Bing’s index as its primary source so if you’ve never set up Bing Webmaster Tools, ChatGPT’s live search layer may not know you’re there.

ChatGPT also has a consensus layer baked in. When it doesn’t have enough data to be confident about a specific source, it defaults to well-established sites: Wikipedia, comparison databases like G2 and Capterra, major publications and even Google Business Profiles for local businesses.

For newer or smaller brands, that means the bar for citation isn’t just “have good content.” It’s “be cited by enough other sources that the model trusts you.”

5. Perplexity

Perplexity is built around transparency. Every answer it generates includes visible citations, which makes it a useful window into how AI models actually evaluate content. CEO Aravind Srinivas confirmed at Bloomberg’s Tech Summit that it processed 780 million queries in May 2025, growing over 20% month-over-month.

Freshness matters more on Perplexity than anywhere else. Research shows an 82% citation rate for content updated within 30 days, compared to 37% for older content. If you wrote a definitive piece two years ago and haven’t touched it since, Perplexity may pass over it for something more recent, even if the older piece is technically better.

6. Claude

Claude is the most conservative citer of the group. Research shows it cites sources in about 39% of queries, compared to 97-98% for Perplexity, averaging around 2 citations per response where Perplexity averages nearly 10. That’s not a flaw in Claude’s design. It reflects a higher confidence threshold: the model only reaches for external sources when it genuinely needs them.

A Profound analysis confirmed that Claude uses Brave Search as its primary web retrieval backend, with an 86.7% overlap between Claude’s cited results and Brave’s top results. So if Bing matters for ChatGPT, Brave matters for Claude. It’s a different index entirely.

The other thing that sets Claude apart is how heavily it weights author credentials. Research from upGrowth found that when Article schema explicitly declares an author entity with verifiable credentials, Claude cites that content at a 94% confidence rate compared to 61% for plain text with no author markup.

This means a named author with a traceable professional background is closer to a prerequisite than a bonus when it comes to Claude citation.

What the LLM models actually read

Here’s something that surprised me when I first looked at this carefully.

AI models don’t read your whole page before deciding whether to cite it. They usually just read the first third.

Kevin Indig’s analysis of 1.2 million ChatGPT answers, covered by Search Engine Land, found that 44.2% of all citations came from content in the first 30% of the page. The model retrieves a passage, scores it, and if the opening doesn’t establish relevance clearly and quickly, the rest of the page doesn’t get a fair hearing.

This has a direct implication for how you structure content. The answer to the question your page is supposed to answer should be in the first two or three paragraphs. Not after a 400-word backstory about why the topic matters.

Think about how you’d answer a colleague’s question face to face. You wouldn’t give them five minutes of context before answering. You’d say the thing, then give the context for why.

That’s what AI-readable content looks like.

Why Structured Data Matters More Than Ever

Schema markup is one of those things people have been told to use for years, and most people either ignore it or implement it once and forget it exists.

In the context of AI search, it’s become one of the most direct signals you can send.

FAQ schema is no longer a quick win for gaining visual SERP real estate on Google but it’s still a quick win for the other LLMs because it creates discrete question-and-answer pairs in your page’s code.

These FAQs map almost exactly to how a language model retrieves and presents information. When you have five well-formed FAQ pairs on a page, you’ve essentially handed the AI five extractable, citable units of content.

It’s not that the schema makes the model like you more. It’s that it makes your content easier to parse correctly. The model doesn’t have to infer what the question is or where the answer ends. You’ve already organized it that way.

Schemas for local businesses, professionals, articles and howto content work similarly. They create structure the model can read with confidence, which means your content is more likely to survive the re-ranking layer.

Google’s E-E-A-T And The Trust Layer of Expertise

Beyond structure and freshness, AI models try to assess something harder to define: trust.

Google frames it as E-E-A-T: Experience, Expertise, Authoritativeness, and Trustworthiness. But trust signals also matter in non-Google AI search. ChatGPT weights sources that appear consistently across multiple reputable citations. Perplexity looks for clear author credentials and transparent sourcing.

Practically, trust comes from a few things.

First, being cited by other sources. If your work shows up in articles by established publications, in forum discussions on Reddit or Quora, in podcast show notes and guest appearances, the model sees a web of endorsement around your name and your content.

Having a clear human behind the content is very important. Bylines with credentials, author pages with background, about pages that explain who you are and why you know what you know. These are signals the model weighs when deciding whether to trust what you’ve written.

Having consistent presence over time. A domain that has published thoughtful, relevant content for three years reads differently to a model than one that launched six months ago.

None of this is new advice exactly. What’s new is that these signals now feed into whether you get cited in an AI answer, not just where you rank on a page full of links.

The Bing Problem Most People Ignore

Here’s a practical issue with Bing that I’ve seen come up often.

If you’ve spent years focused on Google, you’ve probably done almost nothing with Bing, which has had maybe 3-4% of the search market for years, so the logic of ignoring it made sense.

That logic no longer holds because ChatGPT, the most dominant AI search LLM uses Bing’s crawling data.

ChatGPT’s live web retrieval runs primarily through Bing’s index. So does Microsoft Copilot. Between those two platforms, Bing’s index now touches a significant portion of AI-driven searches.

So, if Bing hasn’t crawled your site properly, or if your site is excluded from Bing’s index through some old robots.txt configuration, ChatGPT’s live search simply cannot include you in answers.

Setting up Bing Webmaster Tools is free. It takes about twenty minutes. Submitting your sitemap there ensures Bing is crawling your site. You should also try to get at least a few reviews directly on your Bing Places For Business. It’s low-effort with a disproportionate effect on your AI search visibility.

The AI Crawler Access Problem

While we’re talking about technical access, there’s another one worth checking.

Several site owners discovered in the last couple of years that they were inadvertently blocking AI crawlers. The robots.txt file most sites set up years ago to manage Googlebot access can also block GPTBot (ChatGPT’s crawler), ClaudeBot (Anthropic’s crawler), PerplexityBot, and OAI-SearchBot.

If those bots are blocked, your content is not in their training data or their live retrieval index so no amount of good writing or smart structure overcomes that because the models physically cannot read you.

I see many content creators and professional writers block LLMs because they don’t want them training their models on their data but unfortunately it can have a big effect on your AI search visibility and people finding your content.

Check your robots.txt file and make sure those crawlers are allowed. If you’re using Cloudflare, it’s a good idea to double check your AI crawler bot settings there because it can override the guidelines you’ve put in your robots.txt.

There is movement toward a llms.txt as a standardized approach to providing guidelines specifically to tools like ChatGPT and Claude so I also recommend creating that for your website.

The Key Metric That Replaces Search Rankings

Traditional SEO was organized around one number: where do you rank for this keyword?

AI search doesn’t work that way. There are no positions. You’re cited or you’re not.

The metric that’s replaced rankings is called share of voice: what percentage of AI-generated answers on relevant queries in your space mention or cite your brand?

Measuring this manually is tedious but possible. Pick twenty to thirty questions your ideal audience would ask. Run them through ChatGPT with search enabled, Perplexity, and Google AI Overviews. Track whether your brand is cited, whether competitors are cited instead, and what types of sources are being favored.

Do this every month. You’ll start to see patterns. Which topics are you being cited on? Which ones are going to competitors? Which ones are going to large generalist sites because no specialist has written a clear, structured answer?

Those gaps are opportunities. A well-structured, clearly sourced piece that directly answers a question no one has answered well is exactly what these systems are looking for.

The New Zero Click Reality

One more thing worth naming directly, because I think a lot of people are still building strategies that don’t account for it.

By mid-2025, zero-click searches hit 65% overall, up from 25% five years ago. The person got their answer on the page and they didn’t need to go anywhere.

This has two implications that seem like they’re in tension but actually aren’t.

First: Optimize for citation, not just traffic. Your goal is to be the source that shapes the answer, even if the person never visits your site. Being cited builds brand awareness, authority, and trust in a way that compounds over time, even when it doesn’t generate an immediate click.

Second: when someone does click through from an AI citation, they already trust you. They’ve been told you’re an authority on this topic by a system they trust. That visitor converts at a higher rate than someone who found you through a generic search result.

So the game isn’t “get less traffic.” It’s “get better traffic and broader recognition.” Being cited in AI answers is how you do both.

Where To Start With AI Search Optimization

If you’ve read this far and want to do something concrete today, here’s what I’d suggest.

Check your robots.txt file and make sure you’re not blocking AI crawlers. Set up Bing Webmaster Tools if you haven’t. Then pick five pages on your site that are supposed to answer important questions for your audience. Read the first three paragraphs of each one. Ask yourself honestly: if a researcher read only this much, would they have their answer? If not, rewrite the opening so they would.

That alone puts you ahead of most sites.

The deeper work, building authority through consistent citation by other sources, earning third-party mentions, creating content with clear FAQ structure and proper schema, that takes longer. But the fundamentals aren’t complicated.

Write to answer questions clearly. Put the answer first. Make it easy for machines to read your structure. Get other credible sources to mention you. And make sure the bots can actually find you.

The AI search era rewards the same things good writing has always rewarded: clarity, specificity, and genuine usefulness. The difference now is that a machine is reading it first.