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Designing an auditable AI capability assessment
An AI-assisted assessment can collect conversational evidence while a deterministic ruleset produces the score. This guide develops that architecture through versioned rules, fixtures and an audit record a reviewer can reproduce.
An assessment becomes difficult to defend when a fluent conversation, a score and a polished report arrive as one opaque result. The respondent cannot see which evidence mattered. A reviewer cannot repeat the calculation. A small prompt change may alter both the interview and the score without leaving a useful record.
A safer design gives the AI a limited job. It can conduct a natural conversation, identify candidate evidence and ask for clarification. A deterministic scoring service then applies a versioned ruleset to accepted answers. The report carries the rule version, evidence references and calculation record needed to reproduce it.
The examples here describe an implementation pattern rather than a claim about a particular assessment or engagement. The exact questions and rules still need subject-matter review. The architecture is intended to keep that methodology visible in the software.