Builds evaluation suites with ground-truth answers, automated scoring, and regression detection so you can measure whether model or prompt changes actually improve outcomes before shipping. Without systematic evaluation, teams ship changes that seem better anecdotally but may degrade specific edge cases silently.
Use cases
- Comparing two AI models (or two prompt variations) for a specific task and needing data to decide which to deploy
- Before shipping a prompt change to production and wanting to confirm it does not regress on known edge cases
- Running a weekly model evaluation to detect gradual quality degradation as the model version changes
- Evaluating fine-tuning results by measuring whether the fine-tuned model outperforms the base model on a held-out test set
- Benchmarking AI feature latency and cost alongside quality metrics to make deployment decisions
Key features
- Define task-specific metrics that reflect actual user value—not just generic perplexity or accuracy, but metrics tied to the specific behavior you care about
- Curate an evaluation dataset with diverse, representative inputs and ground-truth answers that reflect what good output looks like for your use case
- Run automated scoring against the evaluation dataset, comparing the new model or prompt against the baseline using statistical tests to determine if differences are significant
- Integrate evaluation runs into CI so that prompt or model changes that regress on eval metrics are blocked before merge
- Report eval results with confidence intervals, not just point estimates—small eval sets with single-run metrics are misleading
When to Use This Skill
- When making a model or prompt change that affects AI feature quality and you need objective data to validate the change
- When shipping AI features to a new domain and needing to confirm quality meets the bar before rollout
- When evaluating AI vendors or model versions and needing a repeatable benchmark to compare against
Expected Output
An evaluation suite with diverse test inputs, automated scoring, statistical significance testing, and a CI integration that blocks regressions.
Frequently Asked Questions
- How many evaluation examples do I need?
- At minimum 50-100 examples for statistical significance on simple tasks. For rare edge cases or high-stakes outputs, you may need hundreds. The key is diversity—100 examples that cover the input space well beats 1,000 repetitive examples.
- What metrics should I use beyond accuracy?
- Depends on the task: for code generation, use unit test pass rates; for summarization, use ROUGE or BERTScore against human references; for factual Q&A, use citation accuracy; for safety, measure false positive and false negative rates. Match the metric to the failure mode you care about.
- How do I detect when my evaluation set is overfitting to my model's strengths?
- Use a held-out test set that is not used during prompt or model development. If performance on the test set diverges significantly from your development eval set, your development set may be biased. Rotate evaluation sets periodically.
Related
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3 Indexed items
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