9 LLM-as-a-judge fundamentals
This chapter covers
- Defining fundamentals required for successful LLM-as-a-judge evaluations
- Illustrating how deterministic metrics break down
- Detailing key engineering considerations to implement LLM-as-a-judge systems
- Defining when to not leverage LLMs for evaluations
LLM-as-a-judge refers to an evaluation approach in which a large language model (LLM) is used to assess, score, or rank the outputs of another model (or multiple models) against defined criteria. Although LLMs are at this point in time commonly used to power product features such as chat interfaces, recommendation explanations, and content generation, in this chapter the model’s role is fundamentally different. The LLM in this context is not generating product output, but instead operating in an evaluative capacity by acting as a scalable, programmatic approach in scenarios where deterministic metrics may fail to capture quality, nuance, or intent.
LLM-as-a-judge extends classic evaluation methods by handling less-specified, more subjective tasks at scale while retaining richer contextual information. But none of this matters if your goals, learning intent, and product dimensions aren’t crystal clear.