When should I use LLM-as-judge versus human scoring?

LLM-as-judge scales cheaply for subjective dimensions like helpfulness or tone, but it can be biased and inconsistent, so the framework recommends calibrating it against a small human-scored sample. High-stakes dimensions like safety usually still need human review.

How big should my test set be?

It depends on how fine a difference you need to detect and how the data slices. The framework recommends a starting size and stresses slicing by input type, difficulty, and edge cases so an aggregate score does not hide failures on important subsets.

What is a baseline and why do I need one?

A baseline is the thing you compare against — your current prompt, a previous model, or a simple rule. Without it, an accuracy number is meaningless; the framework defines how to run the baseline on the same test set so improvements are real and not noise.

Does this run the evaluation for me?

No. It builds the prompt that designs the framework. You then run the framework against your own data and model. This keeps your data private and lets you use whatever tooling you already have.

AI Evaluation Framework Builder

Q: What goes into the generated framework?

A metric definition for each quality dimension, a recommended test-set design with size and slicing, a scoring method per metric (exact match, rubric, or LLM-as-judge), a baseline comparison protocol, and a plan for tracking the metrics over time.

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.

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.