TL;DR: LLM judges and structured decision models such as Jev can help evaluate AI work, but neither removes the need for a clear rubric and human-checked examples. Choose the check based on the decision and the evidence available.
One approach is to ask another language model to judge it. When an AI employee prepares a customer reply or a research brief, a team needs a way to decide whether the work is usable. Another is to use a model designed to return a structured decision.
This guide compares those approaches and explains how to test them against a real business workflow.
What is an AI evaluator?
An evaluator checks an output or a record of work against a defined criterion. A business team might ask whether a support draft uses the approved return policy, whether a research brief includes sources or whether a customer request was assigned to the right queue.
An evaluator is only as useful as its question and the evidence it receives. A confident score cannot establish that a message was sent, a payment was completed or a policy was followed if the evaluator cannot see the relevant record.
How does LLM-as-a-judge work?
In an LLM-as-a-judge workflow, a language model receives the material to evaluate and a rubric. It might be asked to rate a response, identify unsupported claims or explain why an answer needs review. Its ability to produce an explanation can help a person understand a difficult or open-ended judgment.
The verdict can vary between runs, and an explanation may still be wrong. A team needs examples reviewed by people to test whether the judge agrees with the standard it is meant to apply. Check the cost and time of running it at the volume you expect.
What is Jev?
Jev is a decision model from TypeSafe AI that returns structured answers to defined questions rather than generating a free-form explanation. LangChain describes using it to evaluate a record against questions that produce a yes-or-no probability, a choice or a score.
For example, a team could ask whether a draft cites an approved source or select a category for a support interaction. The returned value is useful for software to process, but the question, evidence and decision threshold still need to be designed and tested.
Structured verdicts and explanations
A structured result is convenient when a workflow needs a predictable category or score. It does not, by itself, prove the judgment is correct. A probability or confidence value should be checked against labeled examples from the actual task before it is used to decide what happens next.
A language model can provide written reasoning for a reviewer, which may be helpful when the material is complex. That explanation is also generated output and needs scrutiny. For a simple rule, a deterministic check may be more direct than either kind of model.
Choose the check to match the question
Use a rule when the requirement can be checked reliably, such as whether a required field is present. Consider a model evaluator when the question requires interpretation, such as whether a draft addresses a customer’s actual request. Give the evaluator the relevant request, policy and response.
For a high-impact or ambiguous case, route the evidence to a person. A refund decision, legal commitment or sensitive customer message should follow your organization’s own approval process. Do not assume a model evaluator can replace an authorized decision-maker.
Build a small evaluation set
Collect representative examples and have a qualified person label the expected result. Include straightforward cases, missing information, policy exceptions and examples where a fluent answer is factually wrong. Remove data you are not permitted to use for testing.
Run the same examples through the checks you are considering. Record agreements and disagreements with the human labels, the cost and the time taken. Inspect the failures, especially cases the evaluator rated confidently but incorrectly. Change one criterion at a time so you can understand what improved.
Set a review threshold carefully
A threshold determines when a score triggers a pass, a review or another action. Choose it using your own labeled cases and the consequences of errors. A low-stakes internal draft may tolerate a different review process from a customer-facing commitment.
Recheck the evaluator when the underlying agent, model, policy or data changes. A test on one dataset is evidence for that dataset, not a guarantee that the same threshold will work elsewhere. Keep a way to pause the workflow and inspect a disputed result.
Evaluate the business outcome
Checking the wording of an answer is not the same as checking that the requested work happened. Compare the result with the appropriate source or system record, then make the outcome visible to its owner. Use human review for exceptions and consequences that matter.
Sistava offers AI Employees for business roles with shared context and connected tools. Start with a clear assignment and review its result against your own criteria. Confirm the controls and integrations available for your account rather than assuming a particular evaluator is included.
Give AI work a clear outcome. Explore AI Employees and review their work against your business criteria. Explore AI Employees.