Synthetic Evaluation
Using AI models to evaluate other AI models, generating test cases and scoring outputs automatically. This scales evaluation beyond what human evaluation alone can achieve.
Why It Matters
Synthetic evaluation enables testing at scale — generating thousands of test cases and evaluating responses automatically, catching issues that limited human evaluation would miss.
Example
Using GPT-4 to generate 10,000 diverse test questions and then score another model's responses on correctness, helpfulness, and safety.
Think of it like...
Like using one robot to inspect the work of another robot — the inspector can work 24/7 and check far more units than a human inspector.
Related Terms
Evaluation
The systematic process of measuring an AI model's performance, safety, and reliability using various metrics, benchmarks, and testing methodologies.
Human Evaluation
Using human judges to assess AI model quality on subjective dimensions like helpfulness, coherence, creativity, and safety that automated metrics cannot fully capture.
Benchmark
A standardized test or dataset used to evaluate and compare the performance of AI models. Benchmarks provide consistent metrics that allow fair comparisons between different approaches.
LLM-as-Judge
Using a large language model to evaluate the quality of another model's outputs, replacing or supplementing human evaluators. The judge LLM scores responses on various quality dimensions.
Evaluation Framework
A structured system for measuring AI model performance across multiple dimensions including accuracy, safety, fairness, robustness, and user satisfaction.