Compare AI workflow versions without revealing their labels

Purpose and fit

Use this when a team already has cases and acceptance criteria but needs a fairer comparison between a current workflow and a proposed change. Blinding can reduce expectations attached to a vendor, model or team. The comparison should inform one defined decision, such as whether a revised workflow deserves further testing within its existing scope.

Before you start

Use existing authorised evaluation cases, acceptance rules and outputs where available. For a protocol-only draft, the actual decision and intended comparison are enough; list missing outputs as a prerequisite, not a request to invent scores or a mandatory synthetic dataset.

If your available data uses different fields or terminology, adjust the prompt to match it. State the intended decision, audience and acceptance criteria; ask for essential missing information, and label any non-essential working assumptions. Do not fill factual gaps with invented information.

Required inputs

• An existing evaluation set, task-specific acceptance rules and critical-failure definitions
• Outputs from the versions under comparison, with input and run conditions recorded
• Reviewers who understand the work and a coordinator who can keep the version key separate
• A pre-agreed decision question, comparison method and treatment of ties, missing outputs and disagreements

Practical workflow

1. Write the comparison question and freeze the rules before reviewing results. Specify whether reviewers assess each output independently, express a paired preference, or do both. Preference must not override a critical failure.
2. Generate or collect outputs using comparable inputs and authorised settings. Retain failed and incomplete attempts. If variation matters, define repeat runs in advance and preserve which outputs belong to each run.
3. Have the coordinator replace version names with neutral labels and randomise presentation order. Remove irrelevant identifying metadata without changing substantive content. Keep the mapping inaccessible to reviewers until their judgments are recorded.
4. Ask reviewers to apply the same criteria independently. For paired review, allow a tie, neither acceptable and insufficient evidence. Require a short reason linked to an observable difference or source.
5. Resolve consequential disagreements through source inspection and an adjudicator where needed. Preserve original judgments and the final rationale; do not pressure reviewers to reach artificial agreement.
6. Lock the review record, reveal the version key and summarise results by task type and failure severity. Record whether the evidence supports further testing, retention of the current version or investigation of a particular defect.

Expected output

Expect a blinded-review protocol, blank worksheet and memo template. A coordinator must implement label separation and presentation order outside the reviewer-facing material; this prompt does not establish that a live review was blinded.

A blinded review pack, a restricted mapping key and a comparison memo. Keep the following worksheet separate from version-identifying records:

Case/run ID | presentation order | output label | acceptance result | critical failure | paired preference/tie | evidence and reason | reviewer | disagreement resolution

The comparison memo states the included cases and attempts, counts of wins/ties/losses where applicable, acceptances and critical failures for each version, reviewer disagreements, missing data and the proposed next decision. Report denominators. Do not collapse failures and minor stylistic preferences into a single unexplained average.

Human checks

The coordinator checks that answer keys and version clues have not leaked into review materials. Reviewers declare whether they recognise a version and why; record imperfect blinding rather than claiming a fully blind exercise. Qualified humans adjudicate consequential judgments. Any deployment or expansion still requires its own authorised decision, even when one version performs better on the reviewed cases.

Limitations

Blinding cannot remove all bias or repair an unrepresentative evaluation set. Different length or tone may reveal a version. Repeated outputs from one case are not automatically independent observations. A small preference advantage does not establish statistical significance, production reliability or greater business value.

Responsible use

Use relevant data that exists or that you provide, with accuracy and completeness sufficient for the task. Different AI models and versions may produce different results, and responses may vary between runs. You are responsible for checking and validating the output against your own requirements, source information and acceptance criteria before relying on it.