TL;DR
An automated evaluation method that employs a highly capable large language model to assess, score, and provide qualitative feedback on the outputs of other AI systems.
This method serves as a highly scalable alternative to slow and expensive human evaluation by using structured prompts and scoring rubrics. The judge model receives the input context, the target model's output, and a reference answer, then generates a quantitative score alongside analytical reasoning. While highly effective, practitioners must design judges carefully to control for common biases, such as a preference for longer answers or a tendency of models to favor their own generations.
Why this matters for your business
It allows organizations to rapidly iterate and monitor generative AI applications in production without the bottleneck of manual human review. This drastically lowers the operational costs and time required to validate model updates.
