Web grounding is the practice of basing a language model’s answers on live web content retrieved at query time, so its responses reflect current, verifiable sources rather than only what it learned in training. A grounded model searches the Web for a query, pulls in the relevant pages, and generates its answer from that retrieved content—often citing the sources it used.

In short: web grounding ties a model’s answers to fresh, real web sources fetched at query time, instead of relying on its training data alone.

How web grounding works

The model is given current web content to reason over before it answers:

Search: When a query needs current or factual information, the system searches the Web and gets back relevant, ranked results.