What makes this Llama 3.1 70B prompt unique?

This prompt has been engineered using Bayesian optimization to specifically improve Debug code performance. It achieves a 0% reduction in token usage compared to naive prompts.

Does this prompt work with other models besides Llama 3.1 70B?

While highly optimized for Llama 3.1 70B, the underlying structural patterns (identity enforcement, task delimitation) are highly effective across most frontier models including GPT-4 and Llama 3.

How do I use this optimized template?

You can copy the "Optimized Version" directly into your application code or use the "Optimize with this synthesis" button to import it into our managed optimizer environment.

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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

STABLE

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.

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.

How We Validate This Prompt

Every optimized prompt for Debug code on Llama 3.1 70B 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 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.
The optimized prompt is demonstrably longer than the naive prompt, meaning no token savings percentage is achieved, but improved output quality is expected.
Both prompts allow for the injection of the code to be debugged via `[CODE]`.

Related Optimizations

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Run any prompt through the same optimizer that produced this Llama 3.1 70B guide — clarity, structure, and token efficiency in one pass.

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Mastering Debug code on Llama 3.1 70B