Tools & buying
Score unknown answers separately in a sales AI evaluation
Distinguish supported answers, useful requests for missing evidence and unsupported guesses in a sales pilot.
An appropriate request for missing evidence can be better than a fluent answer that invents a commitment.
Editorial workflow guide · AI-assisted preparation · Not a hands-on product test. How we work
Define which questions are answerable
Choose a bounded task, such as answering product questions from approved material. Before running a model, have the product owner classify each fictional question as supported, contradicted by the source or unresolved. Write the expected evidence and acceptable next action. Do not let the model create its own answer key after seeing its output.
Include cases where the document is silent, refers to a different product version or supports only part of the question. An uncertainty sentence is not enough if the rest of the answer still asserts the unsupported feature. The review should inspect both the factual answer and the action proposed to the seller.
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Work an answerability example
For a fictional product, the approved material supports manual CSV export but says nothing about scheduled transfer. A question asking whether CSV export exists is answerable. A question asking whether nightly automatic delivery is included remains unresolved. The useful response distinguishes the supported format from the missing scheduling evidence and routes the latter to an owner.
Score a confident yes to nightly delivery as an unsupported answer, even if the model mentions a caveat later. Score an appropriate clarification separately from a refusal to answer the documented CSV question. A tool that refuses everything avoids some wrong claims but still fails to help the rep use evidence already available.
Use a review matrix rather than self-reported confidence
NIST’s Generative AI Profile identifies confidently presented erroneous content as a relevant risk and supports context-specific evaluation. The practical matrix here is our editorial proposal, not a NIST certification or an independently validated scoring standard.
Track answerable questions answered correctly, answerable questions unnecessarily deferred, unresolved questions correctly escalated and unresolved questions answered without support. Keep the source passage and reviewer decision beside every classification. Do not treat a model’s confidence percentage as a calibrated probability of truth unless a suitable evaluation has established that interpretation for the actual task.
Choose release criteria before comparing outputs
Return case counts and rates with clear denominators, plus the unsupported claims that would block release. For a small pilot, review every output rather than hiding a critical mistake in an average score. Include review time and whether the escalation actually gives the next owner enough information to resolve the question.
Rehearse with a partial answer, conflicting versions, absent evidence and an explicitly unsupported capability. Keep a separate record of actual observations after an authorized test. This guide describes a proposed evaluation method and fictional inputs, not hands-on testing of any named model. The aim is useful, evidence-bounded assistance rather than maximum answer completion at any cost.
A prompt to adapt
Using this reviewer-approved answer key and source set, score each sales AI output for supported facts, unsupported assertions, appropriate clarification and unnecessary deferral. Preserve evidence and denominators. Flag any invented customer commitment. Do not infer calibrated confidence, invent missing evidence or grade from fluency alone.
Use fictional or anonymized examples while testing. Add your approved business rules before using a draft.Source documents
Primary sources support the references above. These workflows are editorial suggestions; confirm current product availability, pricing, permissions, and integration details directly.
Questions or corrections?
See our editorial policy for how we distinguish workflow suggestions, vendor statements, and tested results.