--- title: "How Do Generative AI Responses Work? (2026)" description: "Knowledge base, web search, query fan-out, source selection... Discover how ChatGPT, Perplexity, and Gemini build their responses step by step." url: "https://vydera.com/en/lab/how-generative-ai-responses-work" lang: "en" published: "2026-01-30" modified: "2026-02-03" alternates: - lang: fr url: https://vydera.com/fr/lab/fonctionnement-reponses-ia-generatives --- [GEO & AEO](https://vydera.com/en/lab-categories/geo-aeo) Written on 30/1/2026 Updated on 3/2/2026 7min # How Do Generative AI Responses Work? [Thibaut LegrandCo-founder - Vydera](https://vydera.com/en/authors/thibaut-legrand) ## Key takeaways - LLMs operate in two modes: knowledge base (training data) or real-time web search (RAG) - AI decides to search the web when it detects uncertainty or needs fresh data - Query fan-out breaks down each question into multiple sub-queries to explore the full semantic field - ChatGPT uses ~25 sources per response vs ~10 for Perplexity, with only 6% of sources in common - Sources come from top 10-30 SERPs: SEO remains the foundation of AEO - AI favors well-structured, recent, and easily extractable content - Structured data helps LLMs understand the context of your content In 2026, a growing number of internet users no longer click on links. They ask their questions directly to ChatGPT, Perplexity, or Gemini and expect a synthesized answer. But how do these AIs build their responses? Where does the information they cite come from? To optimize your visibility in AI engines (what we call **AEO** — Answer Engine Optimization), you first need to understand how they work. That's what we'll break down in this article. ## The Two Response Modes of LLMs When you ask an AI a question, it can respond in two very different ways. Understanding this distinction is fundamental. ### Mode 1: Response from the Knowledge Base In this mode, the AI responds solely from what it learned during its **training**. It doesn't search for information on the Internet. **How does it actually work?** The model was trained on billions of texts: articles, books, websites, forums... During this training, it learned **statistical relationships between words and concepts**. When you ask it a question, it generates a response by predicting the most likely words in that context. The LLM doesn't "know" in the human sense. It has learned patterns and generates coherent text based on those patterns. **The limitations of this mode:** - **Knowledge cutoff**: knowledge stops at the training date - **Hallucinations**: if information is missing, the AI may invent a convincing but false answer - **No verification**: no external source to validate claims ### Mode 2: Response with Web Search (RAG) In this mode, the AI combines its internal knowledge with **external sources** retrieved in real-time from the web. This is called **RAG** (Retrieval-Augmented Generation). This is the mode that matters for AEO. When the AI cites sources, it means it has activated web search. ![GPT Web search](https://vydera.com/_emdash/api/media/file/01M67FF7T697KCTRV4C4WMMJSF.avif) ### When Does AI Decide to Search the Web? AI doesn't systematically launch a search. It first evaluates its **level of uncertainty** about the question asked. **Signals that trigger a web search:** - Queries that require **fresh data** (news, prices, recent events) - Questions that demand **verifiable information** (figures, statistics, precise facts) - Searches related to **e-commerce** (product comparisons, reviews, prices) - Topics where the model detects **uncertainty** in its knowledge Conversely, for general knowledge questions or well-established concepts, AI often responds directly from its knowledge base. ## The Complete Process of a Web Search Response When AI decides to search the web, it follows a multi-step process. This is where it gets interesting for understanding how to appear in its responses. ![Schema of how AI generates a response](https://vydera.com/_emdash/api/media/file/01M67FF8GGFDZY0CRQYPV6JP46.avif) ### Step 1: Prompt Analysis The AI analyzes your question to understand the **search intent**. It identifies: - The main topic - Implicit subtopics - The expected level of detail - Context (location, language, conversation history) ### Step 2: Query Fan-Out (Decomposition into Sub-Queries) This is the key step. The AI doesn't launch a single search matching your question. It **breaks down your prompt into multiple sub-queries** to explore different angles of the topic. **Concrete example:** You ask: *"What's the best SEO strategy for an e-commerce site in 2026?"* The AI generates multiple sub-queries: - "e-commerce SEO strategy 2026" - "SEO trends 2026" - "product page SEO optimization" - "technical SEO e-commerce" - "link building e-commerce" - "Core Web Vitals SEO impact" This mechanism is called **query fan-out**. It allows the AI to cover the entire semantic field of your question. [What are fan-out queries?](https://vydera.com/en/lab/query-fan-out-llm-mechanism) **Key figures:** - ChatGPT launches an average of **2.5 searches** per prompt - ChatGPT uses an average of **25 sources** per response - Perplexity (free version) launches **1 search** and uses about **10 sources** - ChatGPT sometimes formulates its queries **in English**, even for questions in other languages ### Step 3: Search and Source Retrieval The AI sends its sub-queries to search engines. Each LLM uses different sources: - **ChatGPT**: mix of Google + Bing - **Perplexity**: primarily Google - **Gemini**: Google Search - **Copilot**: Bing Retrieved results generally come from the **top 10 to top 30** SERPs. The AI doesn't go looking on page 5 of Google. **Important point for AEO:** to appear in AI responses, you first need to be **well-ranked in SEO** for the sub-queries generated by query fan-out. ### Step 4: Source Evaluation and Selection The AI doesn't treat all sources equally. It evaluates: - **Relevance** to the question - Perceived **reliability** of the site (authority, E-E-A-T) - Content **freshness** (publication/update date) - Content **structure** (ease of information extraction) Well-structured content with clear headings, direct answers, and factual data is prioritized. The AI needs to be able to **easily extract information**. ### Step 5: Synthesis and Response Generation The AI compiles information from different sources to generate a **single, coherent response**. It: - Cross-references information to identify consensus - Reformulates in a fluid, natural style - Adds citations to sources used (depending on the LLM) - Structures the response to be immediately usable ## Diagram: From Prompt to Response 1 ### User Prompt "What's the best SEO strategy for an e-commerce site in 2026?" 2 ### Prompt Analysis The AI identifies the intent (informational), context (e-commerce, SEO) and evaluates its uncertainty level to decide if a web search is needed. 3 ### Query Fan-Out e-commerce SEO strategy SEO trends 2026 technical SEO e-commerce product page optimization link building e-commerce Core Web Vitals impact 4 ### Web Search G B Top 10-30 results per query (~25 sources) 5 ### Source Evaluation Relevance, reliability (E-E-A-T), content freshness, and ease of information extraction. 6 ### Synthesis & Aggregation Cross-referencing information, identifying consensus, and coherent reformulation. ✓ ### Generated Response Structured response with citations to sources used. source-1.com source-2.com source-3.com +22 sources ## Differences Between LLMs Not all LLMs work the same way. Here's what distinguishes the main ones. ### ChatGPT (OpenAI) ChatGPT offers **two modes**: - **Without web search**: responds from its knowledge base (knowledge cutoff) - **With web search**: activates browsing automatically or manually When search is activated, ChatGPT is the most "source-hungry": **25 sources on average** per response, with **2.5 searches** per prompt. Distinctive feature: ChatGPT often formulates part of its queries **in English**, even for questions in other languages. ![ChatGPT Interface](https://vydera.com/_emdash/api/media/file/01M67FF8ZK9Z8N16MFVM5ZD2DV.avif) ### Perplexity Perplexity is **always connected to the web**. That's its purpose: an answer engine, not a chatbot. It primarily uses **Google** as a source and systematically displays **citations** with links to sources. The free version launches about **1 search** per prompt and uses about **10 sources**. Perplexity formulates queries **very close to the original prompt**, unlike ChatGPT which explores more broadly. ![Perplexity Interface](https://vydera.com/_emdash/api/media/file/01M67FF9FZQXAHJCVFXERM5MDT.avif) ### Google AI Overviews / AI Mode Google AI Overviews (formerly SGE) and AI Mode are integrated directly into Google search results. The advantage: they rely on the **complete Google index**. Cited sources are generally those appearing in the classic top 10 SERPs. This is the LLM where **traditional SEO** has the most direct impact on AI visibility. ![AI Mode / AI Overview Google Interface](https://vydera.com/_emdash/api/media/file/01M67FFA7ADVDJJB7W0EPRPT6F.avif) ### Gemini Gemini (Google) works similarly to AI Overviews but in a conversational interface. It uses **Google Search** for its searches and can access real-time data. ![Gemini Interface](https://vydera.com/_emdash/api/media/file/01M67FFAQJP1X60M23M63QJAZ2.avif) ### Claude (Anthropic) Claude primarily operates **without web search** in its standard version. It responds from its knowledge base with a knowledge cutoff date. Some integrations (like Claude in third-party tools) can add search capabilities, but it's not native. ![Claude Interface](https://vydera.com/_emdash/api/media/file/01M67FFB8CXCYA7534XQ8CTQNB.avif) ## What This Means for Your Visibility Now that we understand how it works, what does this concretely mean for appearing in AI responses? ### SEO Remains the Foundation To appear in AI sources, you first need to be **well-ranked on Google and Bing**. LLMs pull their sources from the top 10-30 SERPs. No shortcuts: AEO starts with good SEO. ### Cover the Complete Semantic Field With query fan-out, AI doesn't search for your exact keyword. It explores **the entire semantic field** around the question. Consequence: your content must cover a topic **comprehensively**, not just answer a specific query. Topic clusters and pillar content make complete sense here. ### Make Information Extraction Easy AI needs to be able to **easily extract** information from your content. For this: - **Clear headings** (H2, H3) that clearly announce the content - **Direct answers** to questions in the first paragraphs - **Lists and tables** for factual information - **Short paragraphs** (2-4 sentences max per idea) [AI-friendly content](https://vydera.com/en/lab/optimize-content-ai-citations) is above all **human-friendly** content. If a reader can scan your article and quickly find the information they're looking for, so can AI. ### Structured Data **Structured data** (Schema.org) helps AI understand the nature and context of your content. It allows clear identification of: - Who is the author (credibility, E-E-A-T) - What entity is described (company, product, person) - What are the questions/answers (FAQPage) - What is the publication/update date 👉 We've written a complete guide on this topic: [What Structured Data to Implement for SEO and AEO?](https://vydera.com/en/lab/structured-data-seo-aeo) ### Content Freshness AI favors **recent and updated content**. A 2022 article will have less chance of being cited than content updated in 2026. Remember to update your key content and clearly indicate the last modification date. ## Our Perspective at Vydera At Vydera, we've been helping our clients with AEO for several months. Here's what we observe in the field. **AEO is not a revolution, it's an evolution.** The fundamentals remain the same: create quality content, well-structured, that truly answers users' questions. What changes is the importance of certain signals that were sometimes overlooked: - **Complete semantic coverage** of a topic (not just a keyword) - **Editorial structure** that facilitates extraction - **Structured data** that makes context explicit - **Freshness** and regular updates **What surprises us:** Only **6% of sources** are shared between ChatGPT and Perplexity for the same prompt. This means that to be visible on both, you potentially need to double your efforts. Each LLM has its own selection criteria. **Our advice:** Don't start from scratch. Begin by analyzing how your brand appears (or doesn't) in AI responses for your key queries. Identify the sources cited by LLMs in your niche. Then optimize your existing content before creating new ones. ## Conclusion Generative AI doesn't "search" for an answer like a traditional search engine. It **builds** one by combining its internal knowledge and web sources retrieved in real-time. The **query fan-out** mechanism is central: AI breaks down your question into sub-queries to explore the entire semantic field. To appear in its responses, you need to: 1. **Rank well in SEO** for potential sub-queries 2. **Cover the topic comprehensively** (topic clusters) 3. **Structure content** to facilitate information extraction 4. **Implement structured data** to make context explicit 5. **Maintain content freshness** AEO starts with good SEO. But it requires a more comprehensive approach, focused on semantic coverage and structural quality of content. ## What is the difference between an AI with and without web search? Without web search, AI responds solely from its **training data** with a fixed knowledge cutoff date and risk of hallucination. With web search (RAG mode), it queries **Google or Bing in real-time** to retrieve up-to-date sources and generate a more reliable, well-sourced response. ## What is query fan-out? Query fan-out is the mechanism by which AI **breaks down your question into multiple sub-queries** before launching its searches. For example, a question about e-commerce SEO strategy generates searches on trends, technical SEO, link building, etc. This allows covering the entire topic comprehensively. ## Why do ChatGPT and Perplexity give different answers? Each LLM uses **different search engines** (ChatGPT: Google + Bing / Perplexity: Google), formulates its queries differently, and selects sources according to its own criteria. Result: only **6% of sources are shared** between the two for the same prompt. ## How to appear in generative AI responses? First, you need to **rank well in SEO** because LLMs pull their sources from the top 10-30 Google/Bing results. Then, structure your content for easy extraction (clear headings, direct answers, short paragraphs) and implement **structured data** to provide explicit context. ## Is traditional SEO still useful with AI? Yes, SEO remains **fundamental for AEO**. AI doesn't create its own sources: it retrieves content that already ranks well on search engines. Without SEO visibility on the sub-queries generated by query fan-out, your content won't be included in LLM responses. ![Thibaut Legrand](https://vydera.com/_emdash/api/media/file/01M67FDTFHK9T460SE72BDC1M5.avif) Thibaut LegrandCo-founder - Vydera - [Author page on Vydera](https://vydera.com/en/authors/thibaut-legrand) - [Author on LinkedIn](https://www.linkedin.com/in/legrand-thibaut/) - [Author on X](https://x.com/lgd_thibaut) Others articles ## Read more articles 9/10/2026 SEO ### [Google core updates: 5 questions before you blame the update](https://vydera.com/en/lab/core-update-grille-lecture) 16 Search Console properties and 492 days of daily clicks matched against Google’s official update calendar. Across 6 measurable updates, 70.8% of sites never move beyond their own noise. Read article 7/10/2026 News & Insights ### [AI content and Google: the day the rule changed its name, not its target](https://vydera.com/en/lab/contenu-ia-penalite-google) 166 Search Central blog posts, 2 rater guideline PDFs, 1,772 archive captures. What Google actually writes about AI-produced content. Read article 5/10/2026 SEO ### [E-E-A-T: the signals Google actually reads on your site](https://vydera.com/en/lab/eeat-signaux-mesurables) Eleven E-E-A-T signals measured across 17 French SEO agencies and 170 articles. Published scale, full ranking, self-assessment grid. Read article