The short answer
A useful requirements assistant should expose assumptions rather than silently resolving every ambiguity. Compare models on what they clarify, omit and invent, not on polished formatting.
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 claude vs gpt requirements analysis in a documented setting. No measurements, participants or client outcomes are claimed here.
An illustrative test case
The requirement says urgent cases should get priority without defining urgent. Score whether each model surfaces that missing definition instead of inventing a business policy.
This scenario defines a case to examine; it is not an observed result.
A proposed investigation
Prepare requirements with labelled ambiguities, conflicts and missing acceptance criteria. Give both models identical context and budgets. Blind-review outputs for issue detection, clarification questions and unsupported additions.
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
- Ambiguity detection.
- unsupported assumptions.
- acceptance coverage.
- reviewer agreement.
Retain raw observations and explain exclusions. Human grading needs a documented rubric; acceptance thresholds need a stated rationale.
Interpretation and limitations
The outcome applies to recorded versions and prompts. Reviewer preference for writing style should not determine technical correctness.
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.