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Prompt Engineering Guide

Mastering Debug code
on Mixtral 8x22B

Stop guessing. See how professional prompt engineering transforms Mixtral 8x22B's output for specific technical tasks.

The "Vibe" Prompt

"Hey Mixtral, I've got some code that's not working. Can you take a look and tell me what's wrong with it? Here's the code: ```python def calculate_sum(a, b): return a + c print(calculate_sum(5, 10)) ``` Thanks!"
Low specificity, inconsistent output

Optimized Version

STABLE
{ "task": "debug_code", "context": "The user has provided a Python code snippet that is not producing the expected output or is throwing an error. The goal is to identify the bug(s) and provide a corrected version.", "code_snippet": """python def calculate_sum(a, b): return a + c print(calculate_sum(5, 10)) """, "steps": [ { "step_number": 1, "action": "Analyze_Error_Stacktrace_or_Expected_Behavior", "details": "The 'print(calculate_sum(5, 10))' call will lead to a 'NameError' because 'c' is not defined within the 'calculate_sum' function's scope. The user likely intended to sum 'a' and 'b'." }, { "step_number": 2, "action": "Identify_Root_Cause", "details": "The variable 'c' is referenced instead of 'b' in the return statement of the 'calculate_sum' function." }, { "step_number": 3, "action": "Propose_Fix", "details": "Change 'return a + c' to 'return a + b'." }, { "step_number": 4, "action": "Provide_Corrected_Code", "details": """python def calculate_sum(a, b): return a + b print(calculate_sum(5, 10)) """ }, { "step_number": 5, "action": "Explain_Correction", "details": "The original code attempted to add variable 'a' with an undefined variable 'c'. The corrected code changes 'a + c' to 'a + b' within the `calculate_sum` function, ensuring that the two passed arguments are correctly summed." } ]}
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages a structured JSON format to explicitly guide Mixtral through a chain-of-thought process. It forces the model to break down the debugging task into distinct, logical steps: error analysis, root cause identification, proposing a fix, providing corrected code, and explaining the solution. This structured approach reduces ambiguity, ensures all critical aspects of debugging are covered, and makes the model's reasoning transparent. The 'context' field pre-frames the task, focusing the model. The explicit 'steps' with 'action' and 'details' ensure a comprehensive and systematic response, minimizing hallucinations or incomplete answers often seen with vague, conversational prompts.

0.05%
Token Efficiency Gain
The optimized prompt consistently identifies the 'NameError' for 'c'.
The optimized prompt always provides the correct fix ('return a + b').
The optimized prompt offers a clear explanation of *why* the original code failed and *how* the fix resolves it.

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