Generative AI

Generative AI is a model that produces new content — text, images, code or audio — rather than only classifying existing data.

In detail

Generative models predict what should come next given the input they have seen. That makes them fluent, and also makes them capable of stating something confidently that is not true.

For search, the important consequence is that generative systems assemble answers from sources. The sources they can retrieve, parse and trust decide what gets said and who gets credited.

Example

Asking an assistant to summarise the difference between SEO and SEA produces a new paragraph rather than a list of links.

Why it matters

If generative systems answer your audience's questions, being a retrievable, quotable source becomes a distribution channel of its own.

Common mistakes

Publishing generated text without adding anything a reader could not find elsewhere.
Believing fluency signals accuracy.

Related terms

Large Language Model (LLM)
A large language model is a generative model trained on very large amounts of text to predict and produce language.
AI Search
AI search is any search experience where a generative model answers the query directly instead of only listing links.
Retrieval-Augmented Generation (RAG)
RAG is the pattern of retrieving relevant documents first and then having a language model answer using them.
Generative Engine Optimization (GEO)
GEO is optimising content so generative answer engines can retrieve, understand, quote and attribute it correctly.