Glossary

Grounding (Web Search)

Grounding is an AI assistant's use of live retrieved sources — usually web search — to answer, instead of relying on training memory alone. A grounded answer reflects what current pages say and can cite them; an ungrounded one reflects a model's frozen, dated impression.

Grounding is an important condition in AI-visibility measurement. Consumer products such as ChatGPT, Gemini, and Perplexity can search before answering local questions. An API call without search enabled tests a different configuration and may produce different refusal rates, business names, and citations.

An early Routeless run without grounding showed refusal rates as high as 100%. That result reflected the API configuration rather than the sampled markets. The published research methodology uses grounded assistants to match the consumer products being studied.

An ungrounded model may decline to name the best dentist in Austin because it cannot verify current local facts. If a report does not disclose that search was disabled, readers may mistake the refusal for a general finding about consumer assistants. Record and publish the grounding setting with the results.

Grounding allows current sources to affect an answer before the next model training cycle. When retrieval occurs at answer time, updates to pages, profiles, and other sources can change the available evidence. The timing and effect depend on whether the assistant retrieves those sources.

Grounding also needs a defined comparison condition. Use the same prompt, location, model or product, date range, and conversation state across runs. If one sample searches the web and another does not, report them as different systems instead of combining their results.

When evaluating an AI-visibility report, ask whether web search was enabled and whether the sources are available. Citations can confirm that sources were exposed to the user, though their absence alone does not prove that no retrieval tool was used.

Related terms
  • Retrieval-Augmented Generation (RAG) Retrieval-augmented generation is the technique behind grounded AI answers: before responding, the system retrieves relevant documents — search results, web pages, database records — and generates its answer from them.
  • AI Citations AI citations are the sources an assistant links or names in support of an answer.
  • Knowledge Cutoff A knowledge cutoff is the approximate date after which an AI model's training data contains less or no information.

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