Microsoft-Decision-1: how to use one AI to evaluate another's answers

An assistant checks a report and answers that all orders were released. The table provided, however, still shows two pending items. Before that answer goes on to someone, another step in the system could evaluate whether the conclusion is supported by the available data.

That is one of the possible uses of Microsoft-Decision-1, announced on October 9. Microsoft presents the model for bounded tasks, including classification and evaluation of answers according to criteria. It assigns probabilities to the alternatives it receives and is available in Microsoft Foundry and on OpenRouter. Official announcement.

In the orders example, a useful question would be: “Is the claim that all were released supported by the table?”. The system could offer alternatives such as “supported”, “contradicted” and “insufficient information”.

This scoping helps separate issues that usually show up mixed in a general quality assessment. An answer can be clear and well written, but contain a wrong conclusion. It can also reproduce the data correctly and fail to answer what was asked.

The company would need to choose which criteria matter for that task. Checking support in the sources, coverage of the question and compliance with an instruction are different evaluations. A single overall score can hide which of them failed.

The official model card includes evaluating answers against the evidence presented in the input. It also allows offering an abstention option when that evidence is insufficient. The model doesn't produce a written explanation for the result. Microsoft-Decision-1 model card.

This influences the design of the review. Besides the classification, it's useful to keep the question, the evaluated answer, the sources provided and the criteria used. These records let a person examine the case when there is doubt.

If the report is out of date, the comparison may confirm an answer that is consistent with a base that no longer represents the operation. Checking how current the source is remains a responsibility of the process.

The documentation warns that the wording and order of the options can affect the scores. It recommends validating the model on representative examples and setting usage limits according to the impact of errors. Limitations and best practices.

A pilot could bring together correct answers, answers with known errors and cases where data is missing. Human evaluation would serve as the reference to examine what the model catches and what it lets through.

In this test, two indicators deserve attention: incorrect answers approved and adequate answers sent to review unnecessarily. It's also worth measuring the time the team spends investigating the alerts.

A company could start by using the result to organize a review queue, keeping the final release with the team. Expansion would depend on the evidence accumulated in that specific task.

Microsoft-Decision-1 is still in preview. For managers, the opportunity is to evaluate whether an additional check improves the quality of deliverables and helps locate errors. The evaluator itself needs to prove useful before taking on more responsibility.