Heatmaps Make AI Model Evaluation Results Easier to Present to Stakeholders

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Developers working with AI evaluation frameworks can now use the inspect_viz library to generate heatmaps that visualize model performance across multiple configurations. Heatmaps display a 2D color-coded matrix where intensity reflects accuracy scores, allowing patterns to emerge at a glance without requiring mental math from an audience. The approach is particularly useful in time-pressured settings, such as live meetings where a data science lead must compare several LLM candidates across multiple internal tools. Using the scores_heatmap function in inspect_viz, users can orient comparisons by model or by skill to reveal trends across both dimensions simultaneously. Initial findings from the demonstrated dataset showed that the Gemini 3.6-flash model generally outperformed 3.5-flash-lite, and that adding skill configurations tended to improve evaluation scores.

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