Skip to main content
Back to Prompt Library
implementation

Build Purdue Model Validator with Pydantic AI

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

Linked challenge: Purdue Model OT Cybersecurity Auditor using Pydantic AI

Format
Code-aware
Lines
12
Sections
4
Linked challenge
Purdue Model OT Cybersecurity Auditor using Pydantic AI

Prompt source

Original prompt text with formatting preserved for inspection.

12 lines
4 sections
No variables
0 checklist items
from pydantic import BaseModel, Field
from pydantic_ai import Agent

class Conduit(BaseModel):
    id: str
    source_level: int = Field(ge=0, le=5)
    destination_level: int = Field(ge=0, le=5)
    protocol: str

class AuditReport(BaseModel):
    violating_conduits: list[str]
    severity: str
    compliance_status: str

# Instantiate Pydantic AI agent with AuditReport output type

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.