Hamel Husain publishes tutorial: How to use decision models for Evals
Hamel Husain published a new blog post explaining how to use decision models for Evals, including choosing a decision model for a task, tuning its threshold (don't assume the 0.5 default is appropriate), and interpreting the scores the model produces; the post includes several interactive charts. When reposting it, Ma Dongxi NLP noted that recently emerged decision models include TypeSafe's Jev, Perplexity's Decider, Cloudflare's Clef, and OpenAI's Decisions API; they output probabilities rather than long text and are often used as alternatives to LLM-as-a-Judge.
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The original article centers on how decision models can be used for Evals, offering concrete approaches to model selection, threshold tuning, and score interpretation that can be transferred to evaluation workflows.
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View original postNew blog post 🎉: How To Use Decision Models For Evals In this post, we show you how to choose a decision model for your task, tune its threshold (don't assume the 0.5 default is good!), and interpret its scores. Lots of cool interactive charts here. https://t.co/uYAfte9Kb5
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New blog post 🎉: How To Use Decision Models For Evals In this post, we show you how to choose a decision model for your task, tune its threshold (don't assume the 0.5 default is good!), and interpret its scores. Lots of cool interactive charts here. https://t.co/uYAfte9Kb5
Body · AI translation
Great article!
Recently, "decision models" have emerged, such as:
They output probabilities rather than long text, and are often used as an alternative to LLM-as-a-Judge.
How to use these decision models for Evals—I recommend reading this!