“Engineers ask "how do we measure model accuracy?" before launch - where do you get your first test cases?”
Build an AI eval benchmark from scratch with zero sample data
Recommended AI Agent Skills
Create synthetic evaluation benchmarks, grading rubrics, and automated judge harnesses for AI models.
EvaluationLLM EvalsBenchmarking
Agent task evaluation framework calculating Pass@k, trajectory correctness, and tool invocation fidelity.
AgentsEvalsGoogle AI
GitHub's standard agentic evaluation framework for scoring agent reasoning steps, tool use, and task completion reliability.
Agent EvaluationTestingGitHub
Action framework
- Handcraft 20 core user scenarios (10 standard asks, 5 ambiguous/adversarial probes, 5 out-of-scope queries).
- Define the ground truth target answer and a pass/fail rubric for each test case.
- Have 2 teammates evaluate the test independently - if they disagree, your rubric isn't objective enough yet.
- Run the model through all 20 cases and record the baseline pass rate before tuning prompts or RAG.
- Append a new test case every time you find an unexpected failure or weird output during dogfooding.
Before
"Wait until launch and collect real user queries to evaluate."
After
"Start with 20 curated golden test cases and clear rubrics (10 happy + 5 edge + 5 adversarial), measure 65% baseline pass rate, target 85% for beta release."