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How AI Search Works

TL;DR

AI search works by using large language models to understand questions, retrieve relevant information from the web or its internal knowledge, and generate answers using the most relevant content.

Author: Tom Kelly 5 min reads
How AI Search Works

Introduction

Search is no longer driven only by keywords. AI models like ChatGPT, Gemini, Claude, and Perplexity answer questions by understanding meaning, retrieving relevant information, and generating an answer based on trusted sources. This system is called AI search.

This article is part of the LLM SEO pillar, which teaches how to rank in AI-generated answers.

Let’s break down exactly how AI search works and how models choose the information they respond with.

What Makes AI Search Different From Traditional Search

Traditional search engines:

  • crawl the web
  • index pages
  • rank based on links
  • return a list of URLs

AI search engines:

  • understand natural language
  • retrieve meaning-based chunks
  • use embeddings instead of keywords
  • generate direct answers
  • add citations from sources

In AI search, the model decides what information is useful, not just what pages contain matching keywords.

The Three-Part System Behind AI Search

AI search is built on three core systems:

  1. Natural language understanding
  2. Semantic retrieval
  3. Generative answer construction

Let’s walk through each part.

1. Natural Language Understanding

When a user asks a question, the model:

  • identifies intent
  • breaks the question into concepts
  • determines context
  • maps terms to known entities
  • measures what the user really wants to know

Example question:
“How do LLMs choose citations?”

The model identifies concepts such as:

  • LLMs
  • citations
  • retrieval
  • source selection
  • authority ranking

This semantic understanding is what guides the search process.

2. Semantic Retrieval (How Models “Search”)

This is the most important part for LLM SEO.

AI models do not search based on keywords or backlinks.
They use embeddings, which are mathematical representations of meaning.

Retrieval includes:

Semantic similarity matching

The model finds content that is meaningfully similar to the question.

Chunk-level retrieval

Models do not retrieve entire pages.
They retrieve the best chunks of text.

Topic cluster evaluation

If your content has strong internal links and a pillar structure, the model sees your site as more authoritative.

Schema and structure interpretation

Schema clarifies context, and models favor sites that use it.

Freshness signals

Updated pages rank higher in retrieval.

This is why your pillar structure matters for earning AI citations.

3. Generative Answer Construction

Once the model retrieves relevant chunks, it:

  • analyzes each piece of content
  • ranks them by clarity and authority
  • blends them into a single answer
  • chooses citations for transparency
  • removes redundant or conflicting information

The model does not simply copy content.
It synthesizes the best pieces across all sources.

Why Certain Pages Get Retrieved More Than Others

Models prefer content that is:

Structured clearly

Headings, definitions, and step-by-step explanations improve retrieval.

Focused on a single topic

AI favors depth over breadth.

Pillar-linked content ranks higher.

Supported by schema

Structure helps models understand relationships.

Updated regularly

Freshness increases retrieval probability.

Your site uses all of these signals by design.

How AI Search Works in ChatGPT, Gemini, Claude, and Perplexity

Every model has unique behaviors, but the core process is the same.

ChatGPT uses:

  • semantic similarity
  • retrieval augmentation
  • inline citations
  • high-trust sources for topic authority

Your content becomes a candidate when:

  • it is clear
  • it answers directly
  • you have a strong pillar cluster

Gemini blends:

  • traditional indexing
  • Google’s knowledge graph
  • semantic retrieval
  • content quality signals

If your content is:

  • structured
  • pillar-based
  • updated
  • authoritative

…Gemini will use it in AI Overviews.

Claude uses:

  • deep semantic reasoning
  • concept-level interpretation
  • reliable sources with clear structure

Claude favors:

  • educational content
  • well-organized pages
  • pillar-supported articles

Perplexity is the most transparent AI search engine.

It:

  • crawls continuously
  • cites everything
  • retrieves the best information chunks

Your LLM SEO pillar is perfectly aligned with how Perplexity ranks content.

How Your Site Can Win in AI Search

AI search rewards:

  • clarity
  • structure
  • topic authority
  • semantic density
  • pillar-driven content
  • consistent internal linking

To increase your ranking in AI search:

1. Build deep topic clusters

Your LLM SEO pillar is already structured correctly.

This helps models understand the hierarchy.

3. Use question-based headings

Models retrieve based on question patterns.

4. Update your content regularly

Freshness influences retrieval.

5. Add schema using JSON-LD

Your theme includes Article schema automatically.

6. Make pillar pages the center of the cluster

Your pillar is the anchor node.

Full pillar: LLM SEO

Conclusion

AI search works through semantic understanding, chunk-level retrieval, topic clustering, and structured answers. It rewards content that is clear, organized, and supported by pillar pages. By building a deep LLM SEO cluster, your site becomes a trusted source for AI-generated answers across all major models.

Explore the full LLM SEO pillar:

Frequently Asked Questions

How does AI search differ from traditional search engines?

Traditional search ranks links; AI search retrieves sources and generates an answer with a language model, often citing a few supporting pages and blending facts from multiple documents.

How do AI systems find and access my pages in the first place?

Crawlers discover URLs via links, sitemaps, and feeds. robots.txt and llms.txt guide what models may fetch, while canonical tags help avoid duplicates during indexing.

What is an “embedding index” and why does it matter for AI search?

Embeddings turn text into vectors that capture meaning. AI search stores vectors to quickly find semantically similar passages, not just exact keywords, improving recall and relevance.

What is Retrieval-Augmented Generation (RAG) in AI search results?

RAG first retrieves relevant passages, then lets the model draft an answer grounded in those snippets. The goal is accurate, source-backed responses rather than model-only guesses.

Which signals influence which sources an AI cites or uses to answer?

Relevance to the query, clarity of the passage, freshness, author and site authority, structured data, and consensus with other sources all affect selection.

Does schema markup, FAQs, and tables really help with AI visibility?

Yes. Clean headings, labeled tables, and FAQ blocks create precise chunks that retrieval systems match to common questions, making your page easier to cite.

How important is freshness for ranking in AI answers today?

Very important for time-sensitive topics. Visible update dates, changelogs, and new data increase trust and the likelihood your page is chosen during retrieval.

Do AI answers personalize based on user context or history?

Some assistants use session context and preferences to tailor results. However, most still prioritize broadly useful, well-sourced passages over heavy personalization.

Why do assistants show only a few citations even if many pages helped?

Interfaces limit citations for readability. The system selects a small set of representative, non-duplicative sources that best support the answer’s key claims.

How do AI systems reduce hallucinations or resolve conflicting sources?

They favor grounded snippets, cross-check multiple sources, and downgrade outliers. Clear, authoritative pages with explicit evidence reduce the risk of incorrect synthesis.

How can I measure whether my site appears or is cited in AI search results?

Log screenshots of answers, track referrals from assistant surfaces, and maintain a query list to test regularly. Compare trends after content updates and structural improvements.

What is llms.txt and should I add one to my site for AI search?

llms.txt declares how AI crawlers may use your content. Publishing one clarifies permissions for training vs. retrieval and can improve respectful access to your key pages.

What type of content wins most often in AI-generated answer boxes?

Answer-first pages with definitions, checklists, step-by-steps, and concise evidence. Pair a TL;DR with deeper sections, and interlink related guides around the same entities.

What mistakes reduce my chances of being used by AI search engines?

Burying the answer, thin or outdated content, messy HTML, duplicate URLs without canonicals, missing schema, and weak internal links that hide important subpages.

💡 Try this in ChatGPT

  • Summarize the article "How AI Search Works" from https://www.tomkelly.com/how-ai-search-works/ in 3 bullet points for a board update.
  • Turn the article "How AI Search Works" (https://www.tomkelly.com/how-ai-search-works/) into a 60-second talking script with one example and one CTA.
  • Extract 5 SEO keywords and 3 internal link ideas from "How AI Search Works": https://www.tomkelly.com/how-ai-search-works/.
  • Create 3 tweet ideas and a LinkedIn post that expand on this How To topic using the article at https://www.tomkelly.com/how-ai-search-works/.

Tip: Paste the whole prompt (with the URL) so the AI can fetch context.

Tom Kelly

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