Synthetic Evaluation

Synthetic evaluation is the practice of using AI systems to generate test cases and judge the quality of other AI systems' outputs. Instead of relying solely on human evaluators — who are expensive, slow, and inconsistent — synthetic evaluation uses AI to scale the evaluation process.

Why synthetic evaluation is needed

Evaluating AI model quality is one of the hardest problems in the field. Human evaluation is the gold standard but is expensive, time-consuming, and varies between evaluators. As models become more capable and are applied to more diverse tasks, the volume of evaluation needed outpaces what human teams can handle.

Components of synthetic evaluation

• Synthetic test generation: An AI model generates test prompts, questions, or scenarios. These can be targeted at specific capabilities, edge cases, or failure modes.
• LLM-as-judge: A capable AI model (often a different one from the model being tested) evaluates outputs against defined criteria like accuracy, helpfulness, safety, and coherence.
• Automated scoring rubrics: Structured evaluation criteria that the judge model applies consistently across thousands of test cases.

How LLM-as-judge works

The judge model receives the original prompt, the model's response, and evaluation criteria. It then scores the response on specified dimensions and often provides a justification for its rating. Multiple judge calls can be averaged to reduce noise, similar to using multiple human evaluators.

Advantages of synthetic evaluation

• Scale: Evaluate thousands of outputs in minutes rather than weeks.
• Consistency: The same criteria are applied uniformly across all evaluations.
• Cost: Orders of magnitude cheaper than human evaluation.
• Coverage: Test across a wider range of scenarios than human teams could manage.

Limitations and risks

• Judge bias: AI judges have their own biases — they may prefer verbose responses, favour certain writing styles, or fail to catch subtle errors that humans would notice.
• Circularity: Using AI to evaluate AI can create blind spots where both the model and the judge share the same weaknesses.
• Gaming: If the model being evaluated is similar to the judge, it may inadvertently optimise for the judge's preferences rather than genuine quality.

Best practices

The most robust evaluation combines synthetic and human evaluation. Synthetic evaluation handles scale and coverage, while human evaluation validates that the synthetic approach is measuring what matters.