> ## Documentation Index > Fetch the complete documentation index at: https://handbook.sutro.sh/llms.txt > Use this file to discover all available pages before exploring further. # Judges in Evals: Flip Your Intuition > First-principles responses to common objections about using LLMs to judge LLMs. If you are like most developers, your first instinct may be to reject the idea of using [non-deterministic](/patterns/consistency/determinism) approaches in settings where reliability counts. This is especially true in AI reliability itself: using a model to judge the results of another model feels like fighting fire with fire. This typically comes from a handful of credible doubts. Let us combat these concerns from first principles.
Sutro Turn expert judgment into production-grade AI evals. Sutro provides infrastructure for expert annotation, optimization, and measurement. See how Sutro works
| Doubt | Rebuttal | | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **Intelligence:** there is no good reason to believe another LLM should be smarter or more capable than the model it is evaluating. | The model you are using as a judge is not inherently smarter than the model used to generate the results it is evaluating. But it does not need to be, because a [well-designed judge](/primitives/judges/task-design) is evaluating something much narrower than the task being evaluated. | | **Subjectivity:** you are asking the judge to perform a subjective analysis on something that would otherwise be decided by the expert opinions of your team. | You can ground LLM judges in [expert judgment](/patterns/context/expert-annotation). We will make the bold claim that Sutro offers the best way to do this. | | **Coverage:** there is an infinite range of possible inputs to the candidate model, so it is impossible to test against all possible scenarios. | Continuous distributions are part of the nature of building in AI. Even in a world of true AGI, mistakes and edge cases will be abundant. Discrete assertions feel safer, but building AI systems means working in a probabilistic domain. | | **Non-determinism:** results may not be consistent. The exact same input could result in a different judgment, and mild variations of the same input are even more likely to produce this effect. | We can approximate [consistency](/patterns/consistency) through several inference strategies, and use inconsistency as a tool to understand where we need more coverage. | | **Measurement:** If the judge is another AI model, how can we measure *it's* accuracy? | You can design your judge to be **verifiable** against a corpus of [expert annotations](/patterns/context/expert-annotation). You can independently measure, calibrate, and optimize the judge's performance against this corpus of annotation using *general rules* so it can be trusted on data it's never seen. |