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implementation
Define Pydantic Output Models
Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.
Linked challenge: BNPL Credit Fraud Anomaly Detection with Pydantic AI and Fiddler AI
Format
Code-aware
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Linked challenge
BNPL Credit Fraud Anomaly Detection with Pydantic AI and Fiddler AI
Prompt source
Original prompt text with formatting preserved for inspection.
1 lines
1 sections
No variables
0 checklist items
Import `from pydantic_ai import Agent` and write Pydantic schemas for `BNPLApplicationData` and `BNPLRiskResult`. Configure Pydantic AI agent with dependencies including Fiddler AI logger.
Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.