The short answer

Several individually plausible rewrites can change an obligation, exception or priority. Trace meaning across versions, not just whether the latest document reads well.

The question and scope

This article is an AI-assisted research-design draft for editorial review, not a report of a completed experiment. It asks how to evaluate ai requirements semantic drift in a documented setting. No measurements, participants or client outcomes are claimed here.

An illustrative test case

A requirement permitting an action only after approval becomes a later summary permitting it whenever necessary. Track whether the approval condition survives each transformation.

This scenario defines a case to examine; it is not an observed result.

A proposed investigation

Create requirement chains through summarisation, translation and refinement. Use a fixed set of obligations as the reference. Have blinded reviewers identify additions, deletions and altered conditions after each step.

Record setup versions, inputs and outcomes in a run ledger. Use repeated trials and matched comparison cases; preserve failed runs as well as successful ones.

What to measure

  • Obligation preservation.
  • cumulative changes.
  • missed exceptions.
  • reviewer agreement.

Retain raw observations and explain exclusions. Human grading needs a documented rubric; acceptance thresholds need a stated rationale.

Interpretation and limitations

Reference obligations need an agreed interpretation. Otherwise the experiment can mistake legitimate clarification for drift.

Distinguish a plausible mechanism from a demonstrated effect. Preserve uncertainty when the design cannot eliminate alternative explanations.

A practical review checklist

  • Is the question narrow enough for the available system or dataset?
  • Are the comparison conditions genuinely comparable?
  • Can a reader trace each conclusion to an observation?
  • Are the boundaries and unresolved assumptions visible?
  • Does the decision change if the result is negative or inconclusive?

The publication artifact

The useful deliverable is a reproducible protocol, a measurement table and a decision note explaining what the evidence would change. Add actual results only after the investigation has been conducted and reviewed. This blueprint does not establish a novel finding or a provider-wide claim.

Sources and further reading

These primary resources are starting points for the concepts and controls involved. They do not validate the proposed experiment or its unmeasured outcomes. Check version-specific documentation when implementing the protocol.

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