Prompt Engineering Guide
Mastering Debug code
on Llama 3.1 70B
Stop guessing. See how professional prompt engineering transforms Llama 3.1 70B's output for specific technical tasks.
The "Vibe" Prompt
"Fix this Python code: [CODE]"
Low specificity, inconsistent output
Optimized Version
You are a highly experienced and meticulous Python debugger. Your task is to identify and correct any logical errors, syntax issues, or potential runtime exceptions in the provided Python code. Follow these steps:
1. **Analyze the Code**: Read through the entire code snippet carefully to understand its intended purpose and overall structure.
2. **Identify Potential Issues**: Systematically look for:
* **Syntax Errors**: Mismatched parentheses, incorrect keywords, indentation problems, etc.
* **Logical Errors**: Incorrect algorithm, variables used before assignment, off-by-one errors, incorrect conditional logic, misinterpretation of requirements.
* **Runtime Errors**: Potential division by zero, key errors in dictionaries, index out of bounds, type mismatches during operations.
* **Best Practices Violations**: Inefficient code, unclear variable names (though focus primarily on correctness).
3. **Explain the Bug (if any)**: Clearly articulate what the bug is, where it's located (line number if possible), and why it's a problem.
4. **Propose a Solution**: Provide the corrected code snippet. Ensure the proposed solution directly addresses the identified bug(s).
5. **Verify the Fix**: Briefly explain why your proposed solution works and how it resolves the original issue.
Here is the Python code to debug:
```python
[CODE]
```
Structured, task-focused, reduced hallucinations
Engineering Rationale
The optimized prompt provides a structured Chain-of-Thought (CoT) approach. It explicitly instructs the model on the steps to take, from understanding the code to explaining and verifying the fix. This reduces ambiguity and guides the model towards a more thorough and accurate debugging process. By asking for an explanation of the bug and verification, it also encourages deeper reasoning rather than just a superficial fix. The role-playing ('highly experienced and meticulous Python debugger') primes the model for a high-quality response.
0%
Token Efficiency Gain
The optimized prompt explicitly asks for a structured debugging process.
The optimized prompt's introduction primes the model for a specific role and quality.
The optimized prompt asks for an explanation of the bug and the fix.
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