AI evaluation framework builder
“It seems to work” is not a measurement. Shipping AI features responsibly means defining what good looks like, building a test set, and scoring against a baseline — before and after every change. This builder captures your task and the quality dimensions that matter, then assembles a prompt that designs a rigorous, runnable evaluation framework: metrics, test-set plan, and scoring rubrics.
How it works
You describe the task, list the quality dimensions you care about, name your baseline, and state your evaluation budget. The tool builds a prompt that instructs an LLM to turn those into a concrete framework: a measurable definition for each dimension, a test-set design with size and slicing, a scoring method per metric (exact match, rubric, or calibrated LLM-as-judge), a baseline comparison protocol, and a tracking plan. All generation happens locally in your browser.
The components of a usable eval framework
Metric definitions
A metric is only useful if you could hand it to two independent scorers and get the same result. “Good response” is not a metric. “Contains the correct date, expressed as DD/MM/YYYY, matching the source document” is. The framework prompt asks the LLM to turn each quality dimension you name into a definition precise enough to score consistently.
Test set design
Most teams make their test set too small and too clean. The framework recommends a starting size and, more importantly, stresses slicing: the aggregate score on 200 examples can hide catastrophic failure on a 20-example edge-case slice. The framework prompt asks for slicing by difficulty, input type, and any known edge cases you name (empty inputs, very long inputs, adversarial phrasing).
Scoring methods by dimension
| Dimension type | Appropriate scoring method |
|---|---|
| Factual correctness with a known answer | Exact match or fuzzy match |
| Formatting compliance | Rule-based check |
| Tone, helpfulness, relevance | Rubric-based human scoring or LLM-as-judge |
| Safety and policy compliance | LLM-as-judge with human spot-check |
| Latency | Percentile measurement (P50, P95, P99) |
Baseline comparison
Without a baseline, a score is a number, not a result. The framework builds a protocol for running your comparison baseline — current prompt, previous model, or a simple heuristic — on the same test set at the same temperature, so improvements are measured rather than perceived.
LLM-as-judge: when it works and when it does not
LLM-as-judge scales cheaply for subjective dimensions (tone, helpfulness, coherence) that human scoring cannot cover at volume. It works when the judge model is larger and more capable than the model being evaluated, and when its scoring rubric is specific enough to anchor it. It fails when: the dimensions are too vague (“is this response good?”); the judge has the same biases as the model under test (e.g., preferring its own generation style); or you use it without a human-scored calibration sample. The framework instructs you to validate LLM-as-judge on at least a small human-scored subset before relying on it at scale.
Tips and notes
- Pick dimensions deliberately. Accuracy, faithfulness, tone, safety, and latency are different axes; scoring them together hides regressions.
- Slice your test set. An aggregate score can be high while a critical subset fails — slice by difficulty, input type, and edge cases.
- Calibrate LLM-as-judge. Validate it against a small human-scored sample before trusting it at scale.
- Always run a baseline. A number without a comparison point tells you nothing about whether you improved.
- Re-run evals after every model or prompt change. Improvements on one dimension often regress another; you need the full picture each time.