Fundamentals January 31, 2026 · 10 min read

How AI Search Works: What ChatGPT, Perplexity, and Claude Actually Do

A technical but accessible look at how AI assistants find, evaluate, and present information when users ask for recommendations.

May Chen

May Chen

Strategist, Geonauts

To optimize for AI search, you need to understand how it works. This isn't as mysterious as it seems, and the differences between platforms matter for your strategy.

The Two Types of AI Search

🧠

Type 1: Training-Based

ChatGPT, Claude

These models learned from massive datasets during training. When you ask a question, they draw from that learned knowledge.

🔍

Type 2: Search-Augmented

Perplexity, ChatGPT Browse

These combine AI with real-time web search. They find current information and synthesize it.

Training-Based AI: How It Works

1 Model trained on web pages, books, articles, forums
2 Training creates associations between concepts
3 Your question triggers relevant associations
4 Model generates response from learned patterns

📌 GEO Implications for Training-Based AI

  • • Mentions in training data matter long-term
  • • Can't be updated instantly (there's a knowledge cutoff)
  • • Authoritative sources weighted more heavily
  • • Frequency of positive mentions influences recommendations

Search-Augmented AI: How It Works

1 Your question triggers a web search
2 AI retrieves relevant sources
3 Model synthesizes information from sources
4 Response includes citations to sources used

📌 GEO Implications for Search-Augmented AI

  • • Current web presence matters
  • • Being on sites that rank well in search helps
  • • Clear, structured information gets cited
  • • You can influence results more quickly

What Each Platform Does Differently

💬

ChatGPT

Training-based (without browsing)

Relies primarily on training data. Has extensive knowledge but a cutoff date.

What influences ChatGPT recommendations:

  • • Prevalence in training data (how often you were mentioned)
  • • Association with relevant topics
  • • Sentiment of mentions (positive, negative, neutral)
  • • Authority of sources you appeared in

Strategy: Focus on getting mentioned in authoritative publications likely in training data: Wikipedia, major news sites, industry publications, popular review platforms.

🔮

Perplexity

Search-first with citations

Queries the web, synthesizes results, and cites sources explicitly.

What influences Perplexity recommendations:

  • • Your presence on sites that rank for relevant queries
  • • Clarity of information on those pages
  • • Recency of content
  • • Structured data and clear formatting

Strategy: Optimize for appearing on pages that rank in traditional search. Ensure your information is clear and citable.

🤖

Claude

Training-based, cautious responses

Uses training data similar to ChatGPT but with different sources and methods.

What influences Claude recommendations:

  • • Similar factors to ChatGPT
  • • May weight different source types
  • • Tends toward more cautious, nuanced responses

Strategy: Broad coverage across reputable sources. Claude may be less willing to make strong recommendations without clear supporting evidence.

🔍

Google AI Overviews

Search index + generative AI

Combines their search index with generative AI capabilities.

What influences Google AI responses:

  • • Traditional search ranking factors
  • • Structured data and Knowledge Graph entries
  • • Review and rating data
  • • Content format and clarity

Strategy: Strong SEO foundation plus structured data. Google has the richest real-time data about your business.

How AI Evaluates Businesses

When AI recommends businesses, it evaluates (implicitly or explicitly):

🏆

Authority

  • • Who's talking about you?
  • • Are you mentioned on trusted sites?
  • • Do you have coverage in your industry?
🎯

Relevance

  • • How clearly connected to the query topic?
  • • Do descriptions match what user asked?
  • • Are you mentioned in the right contexts?

Quality Signals

  • • What do reviews say?
  • • Is sentiment positive or negative?
  • • Specific mentions of quality, service?
📅

Recency

(for search-augmented AI)

  • • Is information current?
  • • Are reviews recent?
  • • Has anything changed recently?

Why Consistency Matters

⚠️ The Inconsistency Problem

If your business name is "Joe's Pizza" on Yelp, "Joe's Pizzeria" on Google, and "Joe's Pizza Restaurant" on your website, AI might:

  • Not connect these as the same business
  • Have lower confidence in information
  • Show fragmented or inconsistent details

Consistent information across all sources builds a clearer picture for AI to work with.

Practical Takeaways

1

Diversify your presence

Don't rely on any single platform

2

Prioritize authoritative sources

Industry sites, major review platforms, news coverage

3

Keep information consistent

Same name, address, details everywhere

4

Monitor different AI platforms

They may show different results for the same query

5

Think about training windows

For non-search AI, mentions need time to appear in training data

6

Optimize for search too

Search-augmented AI relies on search rankings

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