Binary LLM Judge Framework
by Hamel Husain & Shreya Shankar • Co-Founders of the 'Build Your Own Evals' Course at Consulting / UC Berkeley
Hamel is a machine learning engineer with experience at GitHub and Airbnb, now a leading AI consultant. Shreya is a computer scientist and researcher at UC Berkeley, specializing in ML operationalization. Together, they run the top-rated course on Maven about building AI evaluations.
🎙️ Episode Context
This episode demystifies 'Evals' (evaluations) for AI products, arguing they are the highest ROI activity for AI teams. Hamel and Shreya demonstrate a practical workflow starting from manual error analysis ('open coding') to building automated 'LLM-as-a-Judge' systems. They challenge the misconception that evals are just unit tests, framing them instead as a continuous data analysis process that replaces traditional PRDs for AI agents.
Problem It Solves
Ambiguity in measuring AI quality. Numeric scales (1-5 stars) are often subjective and inconsistent, making it hard to track progress.
Framework Overview
A strict method for building automated evaluators. Instead of asking an LLM to 'rate this response,' you define a specific failure mode and ask a binary (True/False) question. You then validate this judge against human decisions.
🧠 Framework Structure
Binary Scoring: Force a decision (Tru...
Specific Scope: Judge one specific fa...
Alignment Check: Measure the agreemen...
When to Use
When you have identified a recurring, complex failure mode (via Error Analysis) that cannot be caught by simple code assertions.
Common Mistakes
Trusting the LLM judge immediately without checking if it agrees with human logic, or using generic 'helpfulness' prompts.
Real World Example
Creating a 'Human Handoff Judge' for the leasing bot that specifically checks strictly defined scenarios (e.g., maintenance requests) and outputs a simple True/False.
If the judge says it's wrong, don't just accept it as the gospel... When people lose trust in your evals, they lose trust in you.
— Hamel Husain & Shreya Shankar