Grounding and Enterprise Retrieval

A Gemini model knows a great deal, but it does not know what happened after its training data was collected, and it has never seen a customer’s internal documents, contracts, product catalog, or policies. When an enterprise agent answers a question without access to the right information, it may produce a confident answer that is outdated or simply wrong. In a customer deployment, one incorrect answer about a refund policy or a contract term can undermine trust in the entire system.

Grounding addresses this problem by connecting the model’s answer to a specific source of information at the time of the request. For an FDE, the core decisions are which source to ground on and how to build the retrieval behind it. Interviewers expect candidates to distinguish public web grounding from private enterprise data, and to explain when a managed search service is enough and when a custom retrieval pipeline is justified.

What grounding means

Grounding is the practice of supplying a model with relevant, verifiable information when it generates an answer, and linking the answer back to that information. A grounded answer has two properties that an ungrounded answer lacks. It is based on current, specific content rather than only on what the model learned during training, and it can be traced to a source, which allows users and reviewers to verify it.

Enterprise grounding usually draws on one of two kinds of sources.

Many systems use both. A telecom support agent might ground on the company’s internal plan catalog for pricing questions and on the public web for questions about a newly announced industry regulation. The sources are kept ...