Building AX evals that actually work
Microsoft's blog post, published on July 15, 2026, offers guidance on constructing effective evaluations (evals) for the AX platform. The post focuses on practical strategies to ensure that AI evals produce meaningful and reliable results, avoiding common pitfalls that lead to misleading metrics. It emphasizes the importance of defining clear evaluation criteria, using representative datasets, and iterating on eval designs based on real-world feedback. The post is part of Microsoft's broader effort to help developers integrate AI responsibly into their applications. By following these practices, developers can better assess model performance, identify weaknesses, and improve overall system reliability. The blog does not specify particular tools or versions but provides general principles applicable to AX evals.
Developers can build more reliable AI evaluations, leading to better model quality and trustworthiness.