Model Evals (Model Evaluations)
Model Evals (Model Evaluations) explained
The key question is what must work well for this task. Correct specifications and clear language in product copy differ from the criteria for routing enquiries. A general does not replace that decision.
Build suitable cases: ordinary tasks, missing information and situations requiring a stop or handover. Separate examples used for improvement from cases used for subsequent assessment. Otherwise, you may optimise only for familiar tasks.
Automated checks, expert judgement and user tests answer different questions. Another language model can help grade results but needs assessable criteria itself. Record the model, data, instructions and grading method so comparisons remain meaningful.
Examine serious failures and differences between relevant groups as well as averages. A change may improve one behaviour while worsening another. A passed assessment applies to its scope; it does not guarantee every future input.
Examples
Hypothetical application
A team compares two configurations for product descriptions using the same approved data. It assesses factual accuracy, clarity and invented additions. After selecting a configuration, it tests further products not previously used for adjustment to see whether the findings hold.
Key Points
- Derive criteria from the real task.
- Separate development examples from assessment cases.
- Critically assess model-based graders too.
- Examine averages and serious individual failures separately.
Practical application
Define acceptable outcomes and relevant failures. Keep test cases and grading traceable. Compare changes under suitable conditions and, for agents, also assess the state actually achieved.
Useful measures
Task-specific quality
Fulfilment of previously defined substantive criteria.
Relevant failures
Frequency and severity of failures important to the application.
Comparison stability
Traceable differences between versions and groups of cases.
Common mistakes
- Treating a general score as approval for any application.
- Mixing assessment and training material without control.
- Hiding rare but consequential failures in averages.
Sources and context
- Anthropic: Demystifying evals for AI agents
Tasks, grading and actual outcomes in assessment of AI applications.
Frequently Asked Questions about Model Evals (Model Evaluations)
No. It reflects performance on those tasks and conditions. Suitability for your purpose needs assessment using relevant cases.
Yes, as one evaluation tool. Its judgements can also be wrong or biased and should be compared with expert-assessed examples.
No. Causes may lie in data, instructions, tools or task boundaries. Investigate the cause before choosing a change.
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