Step 1: Ambiguous prompt

initial_prompt = "Explain how to improve Python performance." response = ask_claude(initial_prompt) print("Initial Response:\n", response)

The above prompt might yield a vague answer such as:

“To improve Python performance, you can optimize your code, use efficient algorithms, and leverage external libraries.”

This is correct but lacks actionable detail. Let’s now clarify the prompt to make Claude more specific and complete.

```python
refined_prompt = """
Explain five specific, verifiable methods to improve Python performance.

For each method, include:

- A short description

- A measurable example

- A small code snippet

Return your answer in a structured format.
"""
response = ask_claude(refined_prompt)
print("Clarified Response:\n", response)

By adding quantifiers (five methods), output structure, and measurable examples, the model now produces a detailed, verifiable result.

Programmatic Self-Clarification

In automation pipelines or chat-driven workflows, Claude can be instructed to critique and clarify its own output. This self-check pattern greatly improves consistency and interpretability.

def clarify_response(original_prompt, model_output):
    """Ask Claude to review and clarify its previous response."""
    follow_up = f"""
The previous response may be incomplete or ambiguous.

Original prompt:

{original_prompt}

Response:

{model_output}

Task:

- Identify unclear or missing details.

- Provide a clearer, more complete version of the response.
"""
    return ask_claude(follow_up)