Hamel Husain Publishes Guide on Using Decision Models for Evals and Threshold Tuning

On October 21, renowned AI engineering consultant Hamel Husain, together with Antaripa Saha, published a blog post titled "How To Use Decision Models For Evals," systematically explaining how to apply decision models to AI evaluation.

Confirmed

• The article surveys the recent wave of decision model launches, mentioning products including TypeSafe's Jev, Perplexity's Decider, Liquid's d1, Cloudflare's Clef, and OpenAI's Decisions
• It covers how to choose the right decision model for a specific task and how to tune its decision thresholds
• In the authors' experiments, tuning the threshold delivered further gains of a dozen or so points on top of the baseline
• The authors stress not to assume the default 0.5 threshold works for every task — thresholds should be adjusted per task

Why it matters

Decision models are an emerging model category, but there has been a lack of systematic methodology for using them correctly in evaluation. Hamel Husain is highly influential in the AI evals space, and this long-form post offers practical guidance on model selection and threshold tuning — the takeaway "don't use the default 0.5 threshold" has direct, actionable value for engineers doing Evals.

2026-10-21 ~ 2026-10-21 · 5 related posts

• [source] Hamel Husain: Decision models can replace LLM judges in evals, but only after threshold tuning — HamelHusain · 2026-10-21
• Hamel Husain: How to use decision models for LLM evals, and why the 0.5 threshold default is wrong — HamelHusain · 2026-10-21

3 near-duplicate retellings: HamelHusain · HamelHusain · HamelHusain