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Prompt Engineering Guide

Mastering Debug code
on Cerebras Llama 3.1 70B

Stop guessing. See how professional prompt engineering transforms Cerebras Llama 3.1 70B's output for specific technical tasks.

The "Vibe" Prompt

"Debug this Python code: [insert code here]"
Low specificity, inconsistent output

Optimized Version

STABLE
You are an expert Python debugger. Your goal is to identify and fix bugs in the provided Python code. Follow these steps: 1. **Analyze the Problem:** Briefly explain what the code is supposed to do and what issue you've identified or suspect exists. 2. **Examine the Code:** Carefully review the provided Python code, line by line, looking for syntax errors, logical errors, runtime errors, or common anti-patterns. 3. **Formulate a Hypothesis:** Based on your examination, propose one or more potential causes for the bug. 4. **Suggest a Fix:** Provide the corrected code. Ensure the fix is concise and directly addresses the identified bug. 5. **Explain the Fix:** Clearly explain why your suggested fix works and how it resolves the bug, referencing the original problem and your hypothesis. Also, mention any potential side effects or alternative solutions if applicable. Python Code to Debug: ```python [insert code here] ```
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages chain-of-thought (CoT) by breaking down the debugging process into a series of logical, sequential steps. This guides the model to perform a more thorough analysis rather than just jumping to a solution. Specifically, it encourages 'Analyze the Problem' to ensure understanding, 'Examine the Code' for detailed review, 'Formulate a Hypothesis' for structured thinking about causality, 'Suggest a Fix' for providing the resolution, and 'Explain the Fix' for justifying the changes and demonstrating understanding. This structure mimics an expert debugger's workflow, leading to more accurate and robust debugging. The explicit role definition ('expert Python debugger') also primes the model for better performance.

0%
Token Efficiency Gain
The model should output a step-by-step debugging analysis followed by the corrected code.
The 'Explain the Fix' section should clearly articulate the reasoning behind the changes.
The corrected code should successfully resolve the bug present in the input code.

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