Teams building AI features often reach for an LLM judge early. Need to know if the output is good? Ask another model. It is flexible, quick to set up, and it feels like progress.
But it is also the most expensive, slowest, and least predictable check you own. A useful habit: order your validation from cheapest and most certain to most expensive and most subjective.
A typical ordering
- Structural checks. Does the output parse? Does it match the schema? Are required fields present?
- Rule-based checks. Length limits, banned strings, allowed values, referenced IDs that actually exist in your database, URLs that resolve.
- Consistency checks. Do the numbers in the summary match the numbers in the source? Does the cited passage actually appear in the retrieved documents?
- Model-based judgment. Only now ask a model whether the tone is right or the answer is helpful.
Each stage filters out failures before the next, costlier one runs. That saves money, but the bigger benefit is signal quality. When a judge score drops, you want to know it dropped for a subjective reason, not because half the outputs were malformed JSON that the judge tried to grade anyway.
Why it matters for trust
Deterministic checks are debuggable. When one fails, you can point to the exact rule. Judge failures need interpretation, and judges drift when their own model changes. Keeping the judge for the narrow set of questions only it can answer makes its results easier to believe.
This week
Look at your current evaluation or guardrail pipeline. Find one thing a model is being asked to verify that a few lines of code could verify instead, and move it earlier. You will likely spend less, and understand your failures better.