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How AI Finds Answers

When someone asks an AI about your brand, where does that answer come from? Understanding the different information retrieval methods is essential for targeting your AI optimisation efforts.

In this guide

  • The difference between parametric and retrieval-based knowledge
  • Which AI models use web search
  • What RAG is and why it matters
  • How to optimise for each retrieval method
12 min read Prerequisite: How AI Learns

Two Ways AI Gets Information

AI assistants retrieve information in two fundamentally different ways. Understanding this distinction is critical for your optimisation strategy.

Parametric Knowledge

"Learned" during training

Information encoded in the model's weights during training. This is the model's "memory" of what it learned from training data.

  • Static until model is retrained
  • Subject to knowledge cutoff
  • Can be inaccurate or outdated

Retrieved Knowledge

"Looked up" in real-time

Information fetched from external sources (web search, databases) at query time. This supplements the model's built-in knowledge.

  • Current and up-to-date
  • Depends on search quality
  • Traditional SEO matters here

Which AI Models Use Web Search?

Not all AI assistants have the same capabilities. This table shows which models can search the web and when they do so:

AI Assistant Web Search When It Searches
ChatGPT (Plus/Teams) Yes Automatically for recent topics, or when requested
ChatGPT (Free) Limited Less frequent, relies more on training data
Claude Yes Since March 2025, automatic when relevant
Gemini Yes Deeply integrated with Google Search
Perplexity Always Search-first by design, cites sources
Microsoft Copilot Yes Uses Bing search extensively
Le Chat (Mistral) Yes Toggle-enabled, Deep Research mode available
DeepSeek Yes Internet Search toggle, integrated with reasoning
Meta AI Yes Search and Research modes, powered by Bing

Strategic Implication

Nearly all major AI assistants now use web search.

As of 2025, ChatGPT, Claude, Gemini, Perplexity, Copilot, Le Chat, DeepSeek, and Meta AI all have web search. This means traditional SEO matters across the board, but training data presence still provides an authority advantage.

What is RAG?

RAG stands for Retrieval-Augmented Generation. It's a technique where an AI retrieves relevant documents before generating a response, combining the best of both worlds:

User Query
Retrieve
Augment
Generate

RAG: Query → Search for relevant content → Add to context → Generate response

RAG is why tools like Perplexity can provide up-to-date information with citations. It's also why your SEO still matters in the AI age. If your content ranks well, it's more likely to be retrieved and used in AI responses.

Optimising for Both Methods

Since different AI assistants use different methods, you need a dual strategy:

For Parametric Knowledge (all models, when not searching)

  • Build presence on authoritative sites that feed training data
  • Ensure consistent entity information across the web
  • Create evergreen, factual content about your brand
  • Focus on being mentioned in industry publications

For Retrieved Knowledge (Perplexity, Gemini, Copilot)

  • Maintain strong traditional SEO
  • Structure content with clear answers to common questions
  • Use schema markup for rich results
  • Keep content fresh and recently updated

Technical Implementation

Schema markup helps AI systems understand the entities and relationships in your content. This is particularly important for retrieval-based systems that need to quickly understand what your page is about.

Structured Data Guide

The Future: More Retrieval, Better Citations

The trend is clear: AI assistants are moving toward more retrieval and better source attribution. Perplexity already cites sources. Google's AI Overviews link to websites. Even ChatGPT is becoming more search-integrated.

This is actually good news for brands. It means your existing SEO efforts have compounding value. They help you in traditional search AND in AI-assisted search. The key is adapting your content to be easily retrievable and clearly answering user questions.

Key Takeaway

Don't choose between parametric and retrieved. Optimise for both.

Build authority for training data inclusion while maintaining strong SEO for real-time retrieval. The AI landscape is evolving, and a dual strategy hedges your bets.

Sources