Source Microsoft Research · Published · Open the original ↗
Lab notes
From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances.
Every day, millions of knowledge workers around the world rely on Microsoft Copilot to draft documents, summarize meetings, surface relevant information, and accelerate productivity. Behind the scenes, this is powered by a network of AI systems, and ensuring they work as intended requires a thoughtful approach to evaluation. At Microsoft, we know that high performance doesn’t come from guesswork. It comes from testing, measuring, and iterating constantly.
Healthcare organizations are constantly synthesizing safety signals from across teams so that they can make faster, better decisions that directly influence patient outcomes and regulatory confidence.
They may ask Copilot to summarize a meeting and extract action items for their teams. Within seconds, Copilot delivers a structured summary citing specific discussion points from the transcript. But how do we ensure this response is accurate, complete, and properly grounded in the meeting content? This is where our evaluation system comes in.
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