Model-Based Evals

Model-based evals use a separate AI model to automatically score or assess the outputs of your LLM application. Instead of writing manual rules or relying on human reviewers, you delegate the judgment to another model, a technique commonly known as LLM-as-a-Judge. You write a prompt describing the evaluation criteria, and the judge model rates the response. This approach handles subjective, open-ended quality dimensions that rules cannot capture, while scaling far more cheaply than human review, though it requires careful prompt design to avoid bias and inconsistency in the judges themselves.