Evaluate LLMs and use them responsibly
Test behavior on realistic cases and plan for errors, privacy, and human oversight.
- Build an evaluation approach that measures task quality and important failure modes.
Evaluate a language model on representative tasks with held-out examples and clear success criteria. Combine automated metrics with human review where meaning or safety requires judgment. Inspect errors across user groups and edge cases, and track latency, cost, and refusal behavior as well as task quality. A benchmark score is evidence about a test set, not proof of performance in every real setting.
A small example
1expected = ["yes", "no", "yes"]
2predicted = ["yes", "yes", "yes"]
3correct = sum(a == b for a, b in zip(expected, predicted))
4print(f"Accuracy: {correct / len(expected):.2f}")Accuracy: 0.67
Language models can produce fluent but unsupported content, expose sensitive information, or behave inconsistently under small prompt changes. Minimize sensitive data, test prompt injection and misuse scenarios, restrict tools and permissions, and provide a path for people to review consequential decisions. Re-evaluate when the model, prompt, or data changes.
Key takeaways
Build an evaluation approach that measures task quality and important failure modes.
Measure behavior with realistic examples and inspect important failures.
Lesson quiz
5 questions · pass with 4 correct · up to 50 XP
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