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Multi-LLM Verification with Claude Opus 4.1
Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.
Linked challenge: Global Tax & Legal Compliance Advisor Agent
Format
Code-aware
Lines
18
Sections
4
Linked challenge
Global Tax & Legal Compliance Advisor Agent
Prompt source
Original prompt text with formatting preserved for inspection.
18 lines
4 sections
No variables
1 code block
Extend your agent's workflow. After GPT-4o generates an initial compliance advice using 'query_legal_database', introduce a step where a separate call to Claude Opus 4.1 is made. Claude's role is to critically review GPT-4o's advice for potential biases or inaccuracies, especially regarding complex legal interpretations. Describe the prompt you would use for Claude Opus 4.1 and how you would integrate this verification step programmatically using the OpenAI Agents SDK's conversational flow.
```python
import anthropic
# client = anthropic.Anthropic(api_key="YOUR_CLAUDE_API_KEY")
def verify_advice_with_claude(gpt4o_advice: str, query: str) -> str:
prompt = f"Critically review the following legal advice provided by another AI for potential inaccuracies, biases, or omissions. Focus on clarity and legal correctness.\nOriginal query: {query}\nAdvice to review: {gpt4o_advice}\nYour assessment:"
# response = client.messages.create(
# model="claude-3-opus-20240229", # Or Opus 4.1 if available
# max_tokens=500,
# messages=[
# {"role": "user", "content": prompt}
# ]
# )
# return response.content[0].text
return "Claude's verified assessment."
# Integrating this into the OpenAI Assistant's thread logic would involve a custom callback
# or explicit function call after GPT-4o's initial response, managing the multi-turn.
```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.