Evaluate AI Account Research at the Claim Level

The decision to make

Polished prose can conceal a weak account match, stale information, or an inference presented as a fact. A brief can contain many correct details and still fail if its central buying hypothesis rests on the wrong subsidiary. Evaluate decision-bearing claims separately from writing quality so fluency cannot compensate for a material error. Specify what usable means for the recipient: a seller preparing discovery needs defensible questions, while a campaign team needs confirmed entity scope and evidence that survives publication review.

Build the practical approach

Create a rubric with independent dimensions: entity match, source support, freshness, buying-situation relevance, and separation of observation from inference. Define hard failures for material identity errors, invented facts, and unsupported assertions central to the proposed play. Decompose a brief into atomic claims so a sentence containing several assertions cannot receive one undifferentiated pass. Review the cited passage, its publication date, and the surrounding context. Set freshness requirements by fact type; leadership roles may need different review intervals from a historical corporate event. Build an evaluation set from actual tasks, including sparse information, similar company names, acquisitions, conflicting dates, inaccessible sources, and deliberately unanswerable questions. Have account specialists label examples with a correct answer, acceptable uncertainty, and unacceptable inference. Blind reviewers to model identity when comparing versions. Keep a stable evaluation subset for regression checks and a separate set of recent failures for improvement. Calibrate automated graders against human labels before using them to screen briefs, and retain human adjudication for material errors.

OpenAI recommends task-specific evaluations, typical and edge cases, and calibration of automated scores with human feedback. OpenAI: Evaluation best practices.

The practical workflow

1. Identify material claims
2. Check account identity
3. Inspect cited passages
4. Label unknowns explicitly
5. Calibrate quality scoring

Compare the approaches

Work through an illustrative scenario

Illustrative scenario: a brief says a target account is replacing its core platform and cites a current job posting. The posting requests experience with that platform but does not identify a replacement project. An older interview mentions a modernization ambition. The reviewer must decide whether these observations collectively justify the stronger statement. They do not establish current replacement intent, so the brief keeps the supported hiring observation, dates the older ambition, and proposes a discovery question. The campaign lead also removes the inferred project from audience-selection criteria. The brief is useful after correction, but its first-review factual score records the material failure rather than treating the eventual usable version as an initial success.

Measure whether the work is useful

Define supported-claim precision as material factual claims directly supported by the cited evidence divided by all reviewed material factual claims. Track entity correctness separately because one identity error can invalidate the entire brief. Measure false assertion rate on unanswerable tasks and appropriate abstention rate on tasks where the gold label requires unknown. For completeness, count required answerable items found; do not reward filling intentionally unknown fields. Report error severity, sample size, and reviewer disagreement with any average score. Measure correction minutes from first submission to accepted brief. For automated grading, report agreement on each hard-failure category and review disagreements, rather than relying only on overall agreement dominated by easy passes.

Claude's citations API returns pointers to supplied document passages; these pointers enable inspection of the supporting text. Anthropic: Citations.

Avoid the common failure points

Several articles repeating a press release are not independent corroboration. A current retrieval date does not make an old observation current. A source may support a role or technology mention without supporting a buying intention. Reviewer confidence is not a substitute for evidence. Avoid using an evaluation set made entirely of accounts whose public information is rich. Preserve failed examples instead of replacing them with easier ones. If a specialist supplies missing knowledge during review, distinguish a research failure from an unavailable public fact and record whether that knowledge is permitted in subsequent outputs.

Your next-action checklist

• Claim audit: Inspect decision-bearing claims. Check the limitation: requires specialist review.
• Brief-level rubric: Add hard-failure rules. Check the limitation: can hide one severe error.
• Automated grader: Calibrate against labels. Check the limitation: may disagree with experts.

Use the comparison to choose a bounded next step. Record the evidence, the responsible owner, and the review decision before extending the play to additional accounts.

Questions this guide answers