Grounding AI Answers: Check the Connection to Evidence

Support and truth answer different questions

Suppose an illustrative internal document says that an office opens at nine, and an assistant repeats that time accurately. The answer is supported by the document. If the office changed its opening time yesterday and the document was not updated, the answer is still wrong for someone visiting today. Grounding therefore asks about correspondence with evidence, while factual accuracy also asks whether the evidence reflects the relevant reality. Confusing these questions leads teams to celebrate a faithful summary of an obsolete source or to blame a model for repeating an error the organisation itself published.

Microsoft's groundedness evaluator documentation defines its measure around claims being substantiated by supplied context. That is a useful operational distinction, not a universal definition for every product that uses the word grounding. Ask what information the system was allowed to use and what its evaluation actually measures. A model can know a generally correct fact from training while failing a strict context-only support test. Conversely, a context-supported answer can mislead because the context refers to the wrong country, customer group or product version.

Identify the evidence boundary before checking the answer

Decide which material counts as evidence for the task. In an owned application, this may be the retrieved passages passed into the model. In a public AI answer, you may only see linked sources and cannot fully reconstruct the hidden context. State that limit when diagnosing the system. Do not claim to know that a model ignored a passage simply because the passage exists somewhere on the web. The RAG guide explains why available documents and actually retrieved context are different sets. Support analysis needs the material the answer can reasonably be assessed against.

Preserve version and scope information. A source about business accounts may not support an answer about personal accounts, even if the product name matches. A current page can also describe historical conditions. Check the date attached to the relevant rule rather than treating the webpage's latest update stamp as proof that every statement applies now. Content freshness explains how to maintain changing facts. In grounding work, freshness is one part of source applicability. The central question is whether this evidence supports this claim for this user's situation, including any conditions the response should have preserved.

Break the response into claims that can be tested

A sentence may contain several assertions. “All users can export unlimited records instantly” includes eligibility, quantity and timing claims. A source confirming that administrators can export records supports only part of it. Split the response into manageable claims and identify the supporting passage for each. Use categories such as supported, partly supported, unsupported and contradicted. These are more informative than a single overall impression. The citation quality guide applies a similar method to references. Grounding analysis can also examine answers without visible citations when the supplied context is known.

Pay particular attention to words that strengthen the source. “May” becoming “will”, “some” becoming “all” and “typically” becoming “always” are small linguistic changes with large practical consequences. A summary can also narrow the source incorrectly by omitting one of several eligible groups. Check arithmetic and transformations separately: a source may provide daily values while the answer computes a weekly total. If calculation is allowed, the result can be supported through an explicit derivation, but it should not be presented as a directly quoted source figure. Make the reasoning visible enough that someone can reproduce it.

Look for completeness as well as unsupported additions

An answer can avoid false additions while still failing the task. If the source says cancellation is possible only after an administrator transfers ownership, a response that simply says cancellation is possible leaves out a necessary condition. Every sentence may be technically supportable, but the combined advice is incomplete. Evaluate whether the answer includes the facts needed for the user's decision. Research on verifiability in generative search distinguishes accurate citations from comprehensive citation coverage. The broader lesson for grounding is that support for selected fragments does not establish that the response preserves the complete decision-relevant meaning.

Contradictory sources require an explicit resolution strategy. An older help page and a newer release note may describe different behaviour. The assistant should not blend them into a compromise feature that never existed. If the authoritative relationship is known, use it and explain the applicable version. If it is unresolved, communicate the uncertainty rather than manufacturing certainty. For publishers, source corroboration helps examine whether apparent agreement comes from independent evidence or repeated copying. Agreement among several pages is not sufficient when they all inherit the same obsolete statement from one upstream source.

Diagnose the failure at the right layer

If the evidence was absent from the supplied context, investigate collection coverage, retrieval, filtering or permissions. If the evidence was present but misrepresented, investigate generation instructions, response construction or model behaviour. If the evidence itself was incorrect, correct the source. These categories can overlap, but naming them gives the team a practical starting point. An illustrative returns assistant might retrieve the correct policy yet omit the unopened-package condition. Re-indexing the same source is unlikely to be the most direct repair. The observed failure lies in preserving a condition that was already available.

A different example has the assistant faithfully repeat an old return window because both old and new policies remain eligible in the collection. The repair may involve version metadata, source retirement and retrieval tests, not simply a stronger instruction to be accurate. Keep a record of the question, context, answer and verification result. The AI hallucinations guide discusses unsupported output, while grounding diagnosis shows whether the source chain already contains the error. A precise report saves time because developers, editors and policy owners can see which part of the information path they are being asked to inspect.

Design useful limits for answers

An assistant should be able to say that the evidence does not answer the question. This is different from refusing every difficult request. It can provide the supported portion, identify the missing condition and request the detail needed to proceed. For example, if a policy varies by account type, the assistant can ask which account the user has rather than selecting one arbitrarily. Do not treat absence of evidence as evidence of absence. “The supplied documents do not mention refunds” cannot safely become “Refunds are unavailable” unless a reliable source establishes that negative rule.

For a publishing team, strengthen grounding opportunities through accurate sources, explicit conditions and clear corrections. You cannot directly control every future answer generated by an external model. You can make the organisation's maintained information easier to interpret and verify. Route repeated source-side problems into content governance, especially when ownership or update triggers are unclear. Evaluate an owned system with representative questions and consequential exceptions, then track unsupported additions separately from outdated-source errors. Grounding becomes useful when it identifies a repairable relationship between claims and evidence, rather than serving as a reassuring label attached to any answer that includes links.

Sources and further reading

• Microsoft: groundedness evaluatorGroundedness assesses whether response claims are substantiated by the supplied context.
• Liu et al.: evaluating verifiabilityThe research separates citation precision from citation recall in generative search answers.