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Prompt Engineering Guide

Mastering Code refactoring
on Llama 3.1 405B

Stop guessing. See how professional prompt engineering transforms Llama 3.1 405B's output for specific technical tasks.

The "Vibe" Prompt

"Refactor this Python code: [CODE HERE]"
Low specificity, inconsistent output

Optimized Version

STABLE
You are Llama 3.1 405B, an expert software engineer specializing in Python. Your task is to refactor the provided Python code snippet. Follow these steps meticulously: 1. **Analyze Functionality**: Describe, in a concise bullet summary, the current intent and behavior of the code. Identify any redundant or unclear logic. 2. **Identify Refactoring Opportunities**: Based on your analysis, list specific areas for improvement. Prioritize issues related to readability, maintainability, performance (if applicable and clear from context), and adherence to Pythonic conventions (e.g., PEP 8). Give each opportunity a clear, descriptive name. 3. **Propose Refactoring Strategy**: For each identified opportunity, explain *how* you plan to address it. Detail the specific changes you will make (e.g., 'Extract method `_process_data` to encapsulate data processing logic,' 'Replace `if/elif/else` chain with a dictionary lookup for improved clarity'). 4. **Implement Refactored Code**: Provide the complete, refactored Python code. Ensure it is fully functional and directly addresses the identified opportunities and strategies. 5. **Justify Changes**: Briefly explain, for each major refactoring, why your new implementation is superior to the original, referencing the identified refactoring opportunities. Here is the code to refactor: ```python [CODE HERE] ```
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages chain-of-thought processing and explicit role assignment for Llama 3.1 405B. By breaking down the complex 'refactoring' task into smaller, sequential, and specific sub-tasks (Analyze, Identify, Propose, Implement, Justify), it guides the model through a structured problem-solving process. This reduces ambiguity, encourages thoroughness, and ensures that the model not only provides refactored code but also explains *why* and *how* it made those changes. The 'expert software engineer' role primes the model for high-quality, idiomatic Python. This structured approach mimics human expert problem-solving, leading to more comprehensive and insightful refactorings.

0%
Token Efficiency Gain
The refactored code implements logical improvements identified by the model.
The justification successfully explains the benefits of the refactored code.
The refactoring adheres to common Pythonic conventions and best practices.

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