Eval Datasets & Scoring Methodologies
CoreBuild evaluation datasets and test frameworks using mixed scoring methodologies · Difficulty 3/5
Explanation
An eval is a test set + a scoring method + a metric, run repeatably. Both halves -- the dataset and the scoring method -- need deliberate design, not ad hoc assembly.
Representative Datasets
A usable eval dataset is:
- Representative -- cases drawn from real usage, including edge cases and known failure modes
- Large enough that results aren't noise
- Held out -- a set the design hasn't been tuned on, so measurement stays honest
Without a held-out set, an eval only tells you how well the system fits the examples it was tuned against, not how it will perform on new input.
Scoring Methodologies
Mix scoring methods to fit the task rather than defaulting to one:
| Method | Best for | Tradeoff |
|---|---|---|
| Code / exact-match grading | Structured or verifiable output | Fast, cheap, unambiguous -- but only works where correctness is checkable deterministically |
| LLM-as-judge | Open-ended, subjective quality at scale | Scalable, but the judge itself must be validated against human labels before you rely on it |
| Human evaluation | Nuanced or high-stakes quality | The gold standard, but expensive -- reserve for what automated methods can't judge |
Most production evals combine all three: deterministic checks where possible, an LLM judge for scale on subjective dimensions, and periodic human review to validate the judge and catch what it misses.
Key Takeaways
- An eval = a representative test set + a scoring method + a metric, run repeatably
- A held-out set (not tuned on) is required for an honest read of quality
- Code/exact-match grading is fast and unambiguous but only fits structured/verifiable output
- LLM-as-judge scales subjective grading but must itself be validated against human labels first
- Human evaluation is the gold standard, reserved for nuanced or high-stakes cases
- Mix scoring methodologies to fit the task rather than relying on just one
Glossary Terms
A two-pass architecture where a generator produces output and a separate evaluator assesses it against explicit criteria. For true quality assurance, the evaluator must be a separate Claude instance with independent context — using the same instance creates confirmation bias.
A representative set of test cases -- including edge cases and known failure modes -- that the system design has not been tuned against, used to give an honest read of quality. An eval built only from examples the design was tuned on only measures fit to those examples, not performance on new input; automating the eval to run on every prompt change and model-version bump is what actually catches regressions.
Related Concepts