Evaluate LLM answers against the work users need to do
Combine task tests, human assessment and production monitoring; report errors as well as averages.
A successful demonstration does not show whether an AI feature can handle the intended users’ everyday work. Build an evaluation set from the intended tasks, state what a correct result requires and retain cases where the system should refuse. Use the same tests when changing models, prompts or retrieval.
Choose checks for the task
SC-LY-05 · REV A · 2026.07
Domain and source review
meaning, evidence and consequences
Calibrated model judge
rubric and disagreement review
Task comparisons
identifiers and accepted variants
Code validation
schema, fields and ranges
Combine checks and validate scoring methods against the intended work.
Define a correct result for each task
For invoice extraction, test the supplier, amount, currency and source record. For a support answer, test whether the cited policy applies to the customer and whether the answer invents a commitment. Include Japanese terminology, ambiguous questions, missing information and access restrictions. Separate development examples from held-out cases so tuning does not silently become the test.
Use the check that matches the question
Code can validate a schema, required fields and numeric ranges. Exact comparison works for a known identifier; it needs allowances where equivalent answers have different forms. Similar wording or a high embedding similarity score does not prove factual correctness. Use source verification and domain review where meaning, completeness or consequences matter. There is no universal cost ladder that determines the correct evaluation method.
Calibrate a model used as a judge
Research on LLM judges has documented position, verbosity and self-enhancement biases in studied settings. Do not assume that every judge has the same error rate. Write the rubric, compare scores with qualified human assessments, vary presentation order where relevant and inspect disagreements. Keep judge versions and prompts with the result. Human ratings also need clear criteria and a process for resolving disagreement.
Relate quality to a business decision
Measure outcomes such as correctly completed tasks, corrections, escalations, response time and cost per accepted result. A factuality score does not establish a specific support-ticket deflection rate without measured evidence. Break results down by task and important failure type. A high average can hide an unacceptable error on a small group of requests.
Monitor with an explicit data policy
Decide what logs and feedback may be kept, who can read them and when they are deleted before collecting examples. Minimise or redact sensitive content where appropriate. Review production failures and add suitable cases to the regression set. A negative rating is a signal to investigate, not automatically a correct label. Preserve enough configuration detail to explain which release produced a result.
Sources and further reading
// Key Takeaways
What to remember
- Test the prospect’s task, language and access rules.
- Validate model-judge scores against defined human assessments.
- Link quality to observed business outcomes, not invented conversion ratios.
// FAQ
Frequently asked questions
Does semantic similarity prove an answer is correct?
No. Similar wording or embeddings can still represent a false statement. Check the relevant facts and sources for the task.
Can an LLM judge replace all human evaluation?
A judge may assist scoring, but its rubric, biases and agreement with qualified human assessments need validation. The appropriate oversight depends on the task and consequences.
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Demonstrate your AI product against Japanese business tasks.
Discuss the product version, representative tasks, scoring criteria and failure reporting your team can support. Our engineers can coordinate Japanese demonstrations and evaluations and record the results that support the commercial proposal.