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Understand Output

Interpret runtime outputs to verify correctness and drive downstream actions.

When to use

Use this after every run in local development and CI.

Prerequisites

  • A completed runtime execution

Steps

  1. Inspect diagnostics keys:
diagnostics = run.diagnostics.to_dict()
print(sorted(diagnostics.keys()))
  1. Confirm row progression:
print(diagnostics["stage_rows"])
print(diagnostics["final_rows"])
  1. Confirm plugin loading by kind:
print(diagnostics["plugin_counts"])
  1. Inspect ranked results:
frame = run.dataset.to_polars()
print(frame.select("fit_rank", "fit_score", "title", "company").head())
print(run.dataset.materialize_records(limit=1)[0].to_dict())
print([entry.to_dict() for entry in run.application_plan[:5]])

Expected result

  • fit_score is bounded to [0.0, 1.0]
  • fit_rank starts at 1
  • application_plan contains top-ranked entries up to top_k
  • non_fatal_errors appears only when fail_fast = false and errors occur

Next steps