Training-task & verifier evaluation

Verify candidate task outputs against explicit evidence and expected behavior.

Step 01

Define and Align

Upload sample data, define your output schema, and provide feedback on the hardest cases.

Output schema
VerifiedYes / No

Whether the candidate satisfies the expected task behavior.

  • true
  • false
Failure typesChoose any

Correctness failures found; empty when the output passes.

  • incorrect_result
  • missing_values
  • duplicate_handling
  • constraint_violation
Order sensitiveYes / No

Whether the task explicitly requires a particular output order.

  • true
  • false
app.sutro.sh/functions/training-task-verifier-evaluation
Collecting feedback · 1/3
Verify candidate task outputs against explicit evidence and expected behavior.
Example 1 of 3

Wrong arithmetic

Model input

Task: return 17 + 25. Candidate: 43.

Model output
Verified
Failure typesMultiple values
Order sensitive

Step 02

Model Sweep

Compare quality and cost on your examples, then choose a model or use Sutro’s default.

Compare models on expert agreement and cost. The interactive chart uses illustrative measurements.

Illustrative sweep · select Value or Best to use that model in your function.

Step 03

Deployment

Call Sutro Functions like any other software. Swap the model as soon as something more performant is released.

Selected modelWaiting for sweep results.

Real-time

import sutro as so

input_data = {
    "text": "Task: return 17 + 25. Candidate: 43."
}

result = so.run_function(
    "training-task-verifier-evaluation",
    input_data,
)

Batch

import sutro as so

job_id = so.batch_run_function(
    name="training-task-verifier-evaluation",
    data="training_task_verifier_evaluation.csv",
)
results = so.await_job_completion(job_id)

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