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Define Pydantic AI Underwriting Model

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Build Home Improvement Loan Portfolio Underwriter with Pydantic AI & Agno

Format
Code-aware
Lines
9
Sections
3
Linked challenge
Build Home Improvement Loan Portfolio Underwriter with Pydantic AI & Agno

Prompt source

Original prompt text with formatting preserved for inspection.

9 lines
3 sections
No variables
0 checklist items
from pydantic import BaseModel, Field
from pydantic_ai import Agent
import agno

class UnderwritingResult(BaseModel):
    decision: str = Field(description='APPROVED, REJECTED, or MANUAL_REVIEW')
    cltv: float = Field(description='Combined Loan to Value ratio after renovation')
    arv: float = Field(description='After Repair Value')
    max_allowed_loan: float

agent = Agent('openai:gpt-4o', result_type=UnderwritingResult)

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.