Prove a task
Run every candidate model on your task and prove the cheapest one that clears your bar.
1. Describe the task
Give RedCrown a plain-language task and a few input/output examples. It uses these to build a scored experiment.
Open Prove (/). Paste your task description and a few examples, then click Use my data. If you are signed out, you are redirected to log in and then back to Prove.
Build a dataset from a CSV with your inputs and reference outputs:
redcrown build-dataset --from-csv examples.csv --task summarize --input-col text --reference-col summary --out task.jsonCall scaffold_experiment with a plain-language task and examples to get a validated experiment spec, or pass them directly to prove_task and let it handle scaffolding automatically:
{
"task": "Summarize a customer support ticket in one sentence.",
"examples": [
{"input": "Ticket text here...", "output": "One-sentence summary."}
]
}2. Run and rank
RedCrown fans your task across every candidate model, scores outputs against your examples, and ranks by the cheapest model that clears the quality bar.
After clicking Use my data, Prove runs the experiment automatically and displays the ranked report inline. The winner row is highlighted in green.
redcrown eval task.json --report-json out.jsonThe ranked report prints to the terminal. --report-json also writes the full receipts to out.json for sharing or inspection.
Call prove_task with your task and examples. It runs the full experiment and returns the ranked winner and savings in one call:
{
"task": "Summarize a customer support ticket in one sentence.",
"examples": [
{"input": "Ticket text here...", "output": "One-sentence summary."}
]
}Last verified .