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Prompt Engineering Guide

Mastering Debug code
on Llama 3.1 8B

Stop guessing. See how professional prompt engineering transforms Llama 3.1 8B's output for specific technical tasks.

The "Vibe" Prompt

"Hey Llama, I need help fixing some code. It's not working right. Can you look at this Python code and tell me what's wrong? ```python def calculate_average(numbers): total = 0 for num in numbers: total += numbers # This is the bug, should be num return total / len(numbers) print(calculate_average([1, 2, 3, 4, 5])) ``` What's the bug and how do I fix it?"
Low specificity, inconsistent output

Optimized Version

STABLE
You are an expert Python debugger. Your task is to identify and correct logical errors in the provided Python code. **INPUT CODE:** ```python def calculate_average(numbers): total = 0 for num in numbers: total += numbers # This line contains the error return total / len(numbers) print(calculate_average([1, 2, 3, 4, 5])) ``` **INSTRUCTIONS:** 1. **Analyze the `calculate_average` function:** Trace the execution with the input `[1, 2, 3, 4, 5]`. 2. **Identify the logical error:** Pinpoint the exact line or expression causing incorrect behavior. Explain *why* it's an error based on the function's intended purpose (calculating an average). 3. **Propose a fix:** Provide the corrected version of the erroneous line. 4. **Provide the complete corrected function:** Show the entire `calculate_average` function with the fix applied. 5. **Explain the fix:** Briefly describe how the correction resolves the identified bug. **Expected Output Format:** **BUG IDENTIFICATION:** [Explanation of bug] **PROPOSED FIX:** [Corrected line] **CORRECTED FUNCTION:** ```python [Corrected function code] ``` **EXPLANATION OF FIX:** [Brief explanation]
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages a Chain-of-Thought approach by breaking down the debugging task into distinct, logical steps. It assigns a clear role ('expert Python debugger') and provides explicit instructions for analysis, error identification, proposed fix, and explanation, all within a structured output format. This guides the model to systematically think through the problem rather than just guessing. The structured output also makes it easier for the model to generate a complete and accurate response. The naive prompt is conversational and lacks specific guidance, potentially leading to less comprehensive or accurate responses. The optimized prompt also implicitly reduces token count by requesting specific information rather than open-ended 'tell me what's wrong and how to fix it,' which can sometimes elicit verbose conversational filler from the model.

15%
Token Efficiency Gain
The optimized prompt explicitly asks for identifying the *logical error*.
The optimized prompt requests a *step-by-step analysis* (trace execution).
The optimized prompt demands the *exact corrected line*.

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