Language AI GUIDE
LLM Evaluations
LLM evaluation measures a language model or application against defined tasks and failure conditions.
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Overview
Relevant dimensions can include factual accuracy, instruction following, retrieval use, robustness, cost, and response time. A single preference score rarely captures all of them.
Key takeaways
- Evaluate the full application configuration.
- Validate grading methods themselves.
- Include abstention and adversarial cases.
Deep Dive
Evaluate the system users actually receive. A model with retrieval, tools, and a particular prompt may behave differently from the same model tested alone. Preserve these settings with the evaluation record, including limits on tool calls and retries.
Combine deterministic checks with judgments that require interpretation. Exact matching works for some extracted fields or executable tests, while a summary may need a rubric for evidence and omissions. Write the rubric so different reviewers can apply it consistently, and examine disagreements.
A model can assist with grading, but its judgment is another measurement process with possible biases. Check it against independently reviewed examples, vary answer order where appropriate, and inspect whether it rewards verbosity or style more than correctness. Do not treat one model approving another as independent proof.
Include unanswerable questions, conflicting sources, long-context cases, and malicious instructions in retrieved material when these are relevant. Report results by task and error severity. Retain failed examples as regression cases while refreshing held-out material so the evaluation does not become a memorized target.
04Worked example
Separate helpfulness from factual support
Give a model an invented policy stating only that refunds are available within 14 days.
Ask whether shipping is refunded. A confident answer is unsupported because the policy does not say.
Score an answer that identifies the missing information more highly than an invented policy, even if the invention sounds more helpful.
What it shows
This constructed case evaluates evidence handling rather than fluency.
Real-World Implementation
Grade a document answer on whether every claim is supported by the supplied passage.
Verify generated code through meaningful behavioral tests and review.
Putting it into practice
Why it matters
- Speed and scale. Language workflows can move faster without sacrificing consistency.
- Access and reach. It expands access across languages and communication styles.
- Clearer decisions. Teams can spend more time on judgment while automation handles repetition.
Watch out for
- Hallucinated facts can quietly enter reports, support flows, or research outputs.
- Prompt sensitivity can create inconsistent results across similar requests.
- Sensitive text data may be exposed if access controls are weak.
Next steps
- Define output format, tone, and quality standards before rollout.
- Ground responses with trusted sources whenever accuracy matters.
- Keep a human review checkpoint for high-stakes outputs.
- Track failure patterns and retrain prompts or workflows regularly.
Sources and further reading
- Yen and colleaguesHELMET: evaluating long-context language models
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Frequently asked questions
Can an LLM judge replace all human review?
It can help scale some checks, but its reliability needs validation for the rubric and domain. Consequential or ambiguous cases may require independent review.
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