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Prompt Engineering Guide

Mastering Debug code
on Claude 3.5 Sonnet

Stop guessing. See how professional prompt engineering transforms Claude 3.5 Sonnet's output for specific technical tasks.

The "Vibe" Prompt

"Hey Claude, can you help me debug this Python code? It's giving me an error. Here's the code: [CODE HERE] And the error message is: [ERROR MESSAGE HERE]. Thanks!"
Low specificity, inconsistent output

Optimized Version

STABLE
{ "task": "Debug Python Code", "priority": "High", "context": { "problem_description": "The provided Python code is encountering a runtime error. I need to identify the root cause of the error and provide a corrected version of the code, along with a clear explanation of the fix.", "code_snippet": "[CODE HERE]", "error_message": "[ERROR MESSAGE HERE]", "traceback_info": "[TRACEBACK INFO HERE]" }, "analysis_steps": [ "1. Analyze the 'error_message' and 'traceback_info' to pinpoint the exact line and type of error.", "2. Review the 'code_snippet' focusing on the identified error location and surrounding logic.", "3. Identify potential logical flaws, syntax errors, or incorrect assumptions that lead to the error.", "4. Formulate a hypothesis for the root cause of the error.", "5. Propose a specific, minimal code change to resolve the issue.", "6. Explain the reasoning behind the fix, detailing why the original code failed and how the corrected code addresses the problem.", "7. Provide the fully corrected and runnable code.", "8. Suggest any best practices or improvements related to the issue, if applicable." ], "output_format": { "diagnosis": "[BRIEF SUMMARY OF THE ERROR]", "root_cause": "[DETAILED EXPLANATION OF THE BUG]", "original_problematic_code_line": "[THE SPECIFIC LINE(S) CAUSING THE ERROR]", "proposed_fix_description": "[CLEAR EXPLANATION OF THE SOLUTION]", "corrected_code": "```python\n[CORRECTED CODE HERE]\n```", "explanation": "[DETAILED EXPLANATION OF WHY THE FIX WORKS AND HOW IT PREVENTS RECURRENCE]", "additional_recommendations": "[OPTIONAL: BEST PRACTICES OR FURTHER IMPROVEMENTS]" } }
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages a structured JSON format to explicitly define the task, priority, and all necessary context (code, error, traceback). It then breaks down the debugging process into a chain of thought with 'analysis_steps', guiding the model through a systematic approach rather than a vague request. Finally, it specifies a detailed 'output_format' to ensure the model provides a comprehensive and consistently structured response, including diagnosis, root cause, proposed fix, corrected code, and explanation. This reduces ambiguity, improves the quality and completeness of the debugging output, and makes the model's reasoning more transparent. The inclusion of 'traceback_info' is crucial for effective debugging, which is missing in the naive prompt.

0%
Token Efficiency Gain
The optimized prompt provides clear instructions and examples for each output field, ensuring the model's output is precise and directly usable.
The optimized prompt uses a 'chain-of-thought' approach by outlining specific analysis steps, guiding the model towards a more accurate and reasoned solution.
The optimized prompt explicitly requests 'traceback_info', which is critical for effective code debugging.

How We Validate This Prompt

Every optimized prompt for Debug code on Claude 3.5 Sonnet is scored against a fixed set of evaluation assertions. A revision ships only when it passes all of them, so the 0% token reduction never comes at the cost of output quality.

  • The optimized prompt provides clear instructions and examples for each output field, ensuring the model's output is precise and directly usable.
  • The optimized prompt uses a 'chain-of-thought' approach by outlining specific analysis steps, guiding the model towards a more accurate and reasoned solution.
  • The optimized prompt explicitly requests 'traceback_info', which is critical for effective code debugging.
  • The optimized prompt's structured JSON format makes it easier for the model to parse and respond with relevant information, compared to the conversational 'vibe_prompt'.
  • The optimization focuses on completeness and quality of output, not necessarily token count, hence 'token_savings_pct' is 0 as the optimized prompt is intentionally more verbose to achieve higher quality.

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Optimize your own Debug code prompt

Run any prompt through the same optimizer that produced this Claude 3.5 Sonnet guide — clarity, structure, and token efficiency in one pass.

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