Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 20 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using `Pydantic AI`, define an agent that takes raw policy text as input and attempts to extract structured `TaxExemptionPolicy` objects. Integrate `Gemini 2.5 Pro` as the backbone LLM for this extraction. Ensure your agent is configured to produce outputs that strictly adhere to your `TaxExemptionPolicy` model, using Pydantic AI's built-in validation.
```python
from pydantic_ai import PydanticAI
from google.generativeai.client import get_default_retriever
from google.generativeai.generative_models import GenerativeModel
from google.generativeai.types import FunctionDeclaration
# Initialize Gemini 2.5 Pro (ensure proper setup with your API key)
model = GenerativeModel('gemini-1.5-pro-latest') # Or gemini-2.5-pro if available
class PolicyExtractorAgent(PydanticAI):
def extract_policy(self, policy_text: str) -> TaxExemptionPolicy:
"""Extracts structured tax exemption policy details from raw text."""
return self.llm_call(
func=TaxExemptionPolicy, # Target Pydantic model for structured output
prompt=f"Analyze the following policy text and extract a TaxExemptionPolicy object:\n\n{policy_text}"
)
# Instantiate and test the agent
# agent = PolicyExtractorAgent(llm_model=model)
# policy_data = agent.extract_policy(policy_text='...')
# print(policy_data)
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Global Compliance Intelligence Agent
Responding to headlines about India's evolving tax policies for foreign cloud providers and manufacturers, this challenge involves building an intelligent agent focused on regulatory compliance. Your task is to develop a 'Global Compliance Intelligence Agent' using Pydantic AI. This agent will be capable of ingesting complex legal and policy documents, extracting structured compliance requirements, and generating validated reports or advice for businesses navigating international regulations. The challenge emphasizes the use of Pydantic AI for creating agents that deliver highly structured, validated, and reliable outputs. You will define robust Pydantic models to represent compliance checks, risk assessments, and policy summaries. The agent will leverage advanced natural language understanding with Gemini 2.5 Pro to interpret legal text, use web scraping to access public policy updates, and deploy efficiently using Akash Network. The goal is to provide clear, actionable, and type-safe compliance insights.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.