Ground truth for financial AI

Use it in an afternoon

# Grab a dataset (JSONL: meta line, then one case per line)
curl -s https://worthune.com/api/v1/evals/refinance | head -3

# Every case: {"id", "inputs", "expected"}
# Meta line carries specVersion + tolerances (rel 1e-9, abs 1e-6)

# The eval loop, in pseudocode
for case in dataset:
    answer = your_model_or_agent(case.inputs)   # LLM, agent, your own engine
    for key, expected in case.expected.items():
        assert close(answer[key], expected,
                     rel=1e-9, abs=1e-6)        # same bar our CI holds

Datasets are deterministic for a given spec version — pin one in CI and it never shifts under you. When a spec changes, the changelog says exactly what moved and why. Sentinel and non-finite values are encoded as strings (“Infinity”, “NaN”) per the spec convention.

27 datasets, 250 cases each

Full index, machine-readable: /api/v1/evals. Free with attribution — credit Worthune when you publish results built on these datasets.