How do answer engines like ChatGPT and Perplexity generate their responses?

The short version

Answer engines like ChatGPT and Perplexity generate responses by retrieving relevant information from their training data or live web sources, then using large language models to synthesize that information into natural language answers with citations. ChatGPT primarily relies on its pre-trained knowledge with optional web browsing, while Perplexity actively searches the web in real time and cites sources for each claim.

Prerequisites for understanding answer engine generation

Basic knowledge of how search engines index web pages
Familiarity with large language models (LLMs) as text prediction systems
Understanding that answer engines differ from traditional search engines by providing direct answers instead of link lists

Identify the retrieval method each engine uses

ChatGPT (by OpenAI) generates responses from its pre-trained neural network parameters, which contain knowledge up to its training cutoff date. When web browsing is enabled, it can search Bing for current information. Perplexity uses a retrieval-augmented generation (RAG) approach: it first runs a live web search, extracts relevant passages from the top results, then feeds those passages into an LLM to compose an answer. Google's AI Overviews similarly retrieves from Google's index before generating a summary.

Examine how citations are attached to responses

Perplexity displays numbered citations inline within the answer text and lists the source URLs at the end of each response. ChatGPT shows citations as numbered footnotes when using its Browse feature, linking directly to the source pages. Google AI Overviews places citation links on specific phrases within the generated text. These citation mechanisms allow users to verify claims by visiting the original sources.

Monitor which engines mention and cite your brand

Tools like EdenRank measure whether answer engines mention and cite a brand across ChatGPT, Perplexity, Gemini, and Google AI Overviews. EdenRank tracks both brand mentions (the engine names your brand) and citations (the engine links to your site as a source). This distinction matters because a mention without a citation provides no traffic or backlink value. Monitoring these metrics helps you understand which content earns citations from answer engines.

Common mistakes

Assuming all answer engines use the same generation method. ChatGPT and Perplexity differ fundamentally in how they retrieve information.
Treating a brand mention the same as a citation. A mention without a source link does not drive referral traffic.
Ignoring answer engine visibility because traditional search rankings are strong. Answer engines pull from different sources and may not cite your content even if it ranks well on Google.
Expecting answer engines to always cite the most authoritative source. They prioritize content that is clearly structured, well-cited itself, and matches the query's intent.

Glossary

Retrieval-augmented generation (RAG): A technique where an AI model first retrieves relevant documents from a search index, then uses those documents as context to generate an answer with citations.
Citation: A source link provided by an answer engine alongside a specific claim, allowing users to verify the information.
Mention: When an answer engine includes a brand name in its response text without necessarily linking to that brand's website.
Training data cutoff: The date after which a pre-trained model like ChatGPT has no knowledge of events or information, unless it uses live web browsing.