Overview: the main AI search engines
The market for AI search engines is growing fast. For businesses, it is essential to understand how the different systems work and by which criteria they select sources. Once you know the mechanisms, you can optimize your content specifically for GEO and LLMO.
The three dominant AI search engines in 2026 are ChatGPT (with built-in web search), Perplexity (a dedicated AI search engine) and Google Gemini (with AI Overviews in the search results). Each has its own strengths and selection mechanisms.
How AI search works: from retrieval to citation
All AI search engines use a variant of retrieval-augmented generation (RAG). The process has three phases:
- Retrieval - The system searches an index and identifies potentially relevant sources
- Ranking - The sources found are scored by relevance, authority and trustworthiness
- Generation - The language model generates an answer and cites the highest-scoring sources
How these factors are weighted differs considerably from platform to platform:
ChatGPT: authority and freshness
ChatGPT uses Bing and its own crawlers as its search index. It prefers sources with high domain authority, current content and clear topical relevance. Particularly effective: well-structured FAQ sections and definitions that ChatGPT can use directly as building blocks for its answers.
Perplexity: transparency and variety
Perplexity is the most transparent AI search engine. It shows every source it uses as a numbered reference and typically combines 4–6 different sources per answer. Perplexity prefers sources with unique information, data and statistics. If you offer original research or exclusive data, you have an advantage here.
Google Gemini / AI Overviews: the Google index as the basis
Google AI Overviews are based on the existing Google search index. Sources that already rank well on Google therefore have a natural advantage. Schema markup, E-E-A-T signals and topical cluster relevance also play a major role.
Signals that influence source selection on every platform
Despite the differences, some criteria matter for all AI search engines. These are the factors you can control:
- Clear page structure - An H2/H3 hierarchy, lists and tables make parsing easier
- Quotable statements - Specific facts, numbers and definitions in 2–3 sentences
- Freshness - Regularly updated content with a visible modification date
- E-E-A-T signals - Authorship, expertise and trustworthiness
- Schema markup - Structured data as a communication channel to the AI
Every AI search engine has its own rules. But the basic principles are universal: trust, structure and real expertise win on every platform.
Go deeper with our article LLMO vs. SEO and find out why the two strategies belong together. Our GEO/LLMO audit analyzes your visibility on all relevant AI platforms. As an SEO & GEO agency, we optimize your presence across systems.
Also learn how to measure your AI visibility systematically and what role llms.txt and structured data play in source selection. If you want to apply this right away, our GEO optimization in 7 steps shows you how to build your content for the way AI systems select sources. A look ahead: with agentic resource discovery (ARD), source selection is shifting from the answer alone toward verified capabilities that an AI agent calls directly.