Do “answer engine” and “generative engine” mean the same thing?

“Answer engine” and “generative engine” largely describe the same kind of AI system: one that retrieves sources for a question and generates a summarized answer with source references. Perplexity, for example, calls itself an answer engine, and research uses both terms.

Is an AI chatbot on a company’s own website also a generative engine?

Technically, a website chatbot can work like a generative engine if it retrieves relevant content for each question, for example from the company’s own website or documentation, and has a language model summarize it into an answer. In the context of GEO, however, generative engines are publicly available AI systems whose answers an individual business does not control.

When did the first generative engines appear?

Among the first publicly available generative engines were Perplexity, released in December 2022, and the chat feature of the new Bing, known as Bing Chat, which Microsoft launched as a limited preview in February 2023 and which is now called Microsoft Copilot. Research described the underlying principle of basing a language model’s answers on retrieved documents as early as 2020, under the name retrieval-augmented generation.

Generative engine: AI answers from many sources

Generative engines are the AI systems that GEO (Generative Engine Optimization) is named after. The term is used in the research paper “GEO: Generative Engine Optimization,” published in November 2023, which studies such systems and also gave the discipline its name. People who read an answer from a generative engine get a finished text and don’t have to open the pages behind it themselves.

How an answer is built

Every generative engine works differently in detail. The 2023 study sketches a representative workflow that, simplified, comes down to three steps:

Preparing the question: A language model derives one or more simpler queries from the question. Splitting a question into several subqueries is called query fan-out.
Retrieving sources: For these queries, the system retrieves matching web pages, usually from a web index—a collection of pages that crawlers (programs that read web pages automatically) have captured in advance.
Writing the answer: A language model summarizes the retrieved content into one answer and points to the pages it used, for example with links or footnotes. These source references are called AI citations.

This principle—basing an answer on content retrieved beforehand—is known as retrieval-augmented generation (RAG). Which pages a generative engine selects and how it weighs them is up to each provider; the methods are proprietary and largely opaque from the outside. The researchers behind the study point out that this makes it difficult for anyone who publishes content to understand how that content is processed and presented.

Generative engine or language model alone

Not every AI answer comes from a generative engine in the narrow sense. When a language model answers a question solely from what it learned during training—its parametric knowledge—it retrieves no content. This path still matters for GEO: what a model learned about an offering during training also shapes its answers, for example as a mention without a source reference.

Many AI assistants combine both paths and decide for each question whether to retrieve content from the web as well. Only then do they work as a generative engine. In a broader sense, the term is also used for AI assistants as a whole.

Examples

These AI systems, for example, work as generative engines when they retrieve web content:

AI assistants: ChatGPT, Gemini, and Claude retrieve web content depending on the question and can link to the sources they used; in ChatGPT, this capability is called ChatGPT search. According to its own description, Perplexity bases its answers on content it finds on the web and adds source references to them. Microsoft Copilot draws on web content through Bing when that content improves an answer.
AI answers on Google: Google AI Overviews and Google AI Mode summarize information from multiple web pages into one answer with links.

What this means for businesses

In a generative engine, a website can only serve as a source if it is among the retrieved content. How well that works depends, among other things, on the crawlability and machine readability of its pages and on whether individual sections answer a question clearly on their own—their citability. Generative engines are not free of errors: an October 2024 preprint, a study released ahead of peer review, examined three such systems and found frequent hallucinations and inaccurate citations in the versions tested at the time, among other problems. So even when an answer cites a website, it does not always reproduce that website’s content correctly.

Generative engine

Definition

How an answer is built

Generative engine or language model alone

Examples

What this means for businesses

Frequently asked questions