LLM-as-Judge Eval
What is an LLM-as-Judge eval?
LLM-as-Judge evals are a type of automated evaluation where you use a second LLM to assess the quality of your primary LLM's output. This evaluation strategy is one of three common approaches—alongside golden datasets and code-based assertions—for measuring AI product quality.
LLM-as-Judge evals are particularly valuable for evaluating subjective qualities or complex criteria that would be difficult to assess programmatically, such as whether questions are leading, whether responses are appropriate in tone, or whether outputs meet domain-specific quality standards.
How do teams develop LLM-as-Judge evals?
Teams typically build LLM-as-Judge evals through a rigorous error analysis process rooted in grounded theory. Rather than starting with hypothetical failure modes, teams annotate actual traces from their system, identify patterns in the data, and let common error categories emerge from what they observe.
Once teams identify the most common error categories, they design specific LLM-as-Judge evals to detect those issues. Each eval targets a particular failure mode—like suggesting leading questions or providing inappropriate advice—allowing teams to systematically measure whether improvements reduce those specific errors.
Why are LLM-as-Judge evals valuable for AI products?
LLM-as-Judge evals make it possible to measure open-ended AI systems without massive human labeling costs. For systems that generate varied, creative, or domain-specific responses, programmatic evaluation would be extremely difficult and comprehensive human review would be prohibitively expensive.
These evals enable real-time quality measurement at scale. Teams can run hundreds or thousands of evaluations quickly, getting immediate feedback on whether prompt changes, model adjustments, or other improvements actually enhance quality. This supports rapid iteration and systematic improvement of AI products.
Learn more:
- Building My First AI Product: 6 Lessons from My 90-Day Deep Dive
- Building Trainline's AI Travel Assistant: How a 25-Year-Old Company Went Agentic
Related terms: