Plausibility is not truth
TruthfulQA studies whether language models reproduce common falsehoods when answering questions. Its historical results should not be treated as current rankings, but the underlying distinction remains important: plausible language and truthful content are different evaluation targets.
In a product, a hallucination may look like an invented feature, a nonexistent citation, or a confident answer unsupported by the available evidence. The remedy depends on the failure. A missing source is different from a calculation error or a response that combines facts from two incompatible versions.
Technical foundation: Lin et al.: TruthfulQA
Specify the evidence standard
Decide which statements require authoritative support. Product availability should come from the catalogue, order status from the order system, and documentation claims from a relevant release reference. A conversational model should not substitute its recollection when those records are available.
Make the answer distinguish facts from suggestions. For example, a design assistant can propose that a sidebar might improve navigation, but it should not claim that a particular theme already includes that sidebar without inspecting the theme. This distinction lets the product remain creative without misleading users.
Check citations at the claim level
A list of impressive links at the bottom of an answer is not sufficient. Inspect whether each important claim is actually supported by the passage associated with it. Generated source titles and URLs can also be wrong, so resolve source identifiers through the application where possible.
For calculations, use deterministic code and include the inputs needed to understand the result. For structured facts, validate against records. A second model may help review an answer, but agreement between models does not independently establish truth. Evidence should remain available to a person.
Make uncertainty actionable
An unhelpful fallback says only that the assistant cannot answer. A useful fallback explains which information is missing, shows the closest relevant source, or asks a focused question. If the user's request is ambiguous, clarification can be better than producing a broad answer that quietly chooses the wrong interpretation.
Test the temptation to answer. Include nonexistent product names, outdated features, contradictory documents, and questions whose premise is false. Reward a well-supported correction or an appropriate abstention. A feature optimized only for answer rate can become less trustworthy as it gets more verbose.
A grounded-answer checklist
- Use an authoritative source for each consequential fact.
- Separate observed capabilities from proposed ideas.
- Verify links and the claims they support.
- Calculate exact values outside free-form generation.
- Make missing evidence and next steps visible.
