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Prompt Engineering Guide

Mastering Code refactoring
on Qwen 2.5 72B

Stop guessing. See how professional prompt engineering transforms Qwen 2.5 72B's output for specific technical tasks.

The "Vibe" Prompt

"Refactor this Python code: [insert Python code here] Make it more readable and efficient. Give me the refactored code."
Low specificity, inconsistent output

Optimized Version

STABLE
You are an expert Python developer with a deep understanding of clean code principles, design patterns, and performance optimization. Your task is to refactor the provided Python code snippet. Follow these steps meticulously: 1. **Understand the Current Logic:** Analyze the existing code to fully grasp its intended functionality, identify potential edge cases, and recognize any hidden assumptions. 2. **Identify Refactoring Opportunities:** Based on your understanding, pinpoint specific areas for improvement, such as: - **Readability:** Confusing variable names, long functions, lack of comments, complex nested structures. - **Efficiency:** Redundant computations, inefficient data structures, unnecessary loops, I/O bottlenecks. - **Maintainability:** Tight coupling, lack of abstraction, duplicated code (DRY principle violations). - **Pythonic Idioms:** Non-idiomatic Python constructs that can be replaced with more Pythonic alternatives. 3. **Propose Refactoring Strategy (CoT):** For each identified opportunity, briefly explain *why* it's an issue and *how* you plan to refactor it. For example: 'Issue: `data_list` naming is ambiguous. Strategy: Rename to `processed_records` for clarity.' or 'Issue: Nested loops lead to O(N^2) complexity. Strategy: Use a dictionary for O(N) lookup.' 4. **Implement Refactored Code:** Write the complete, refactored Python code. Ensure it adheres to PEP 8 guidelines. 5. **Provide a Summary of Changes:** Briefly explain the key improvements made and how they address the identified issues. Highlight any significant performance or readability gains. Here is the Python code to refactor: ```python [insert Python code here] ```
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt works better because it guides the model through a structured thought process (chain-of-thought) before generating the code. It explicitly asks the model to understand, identify problems, strategize solutions, implement, and then summarize. This mimics how a human expert would approach refactoring. By defining clear roles and steps, it reduces ambiguity and increases the likelihood of producing high-quality, well-reasoned refactoring. It also ensures adherence to best practices like PEP 8. The explicit identification of specific refactoring categories (readability, efficiency, maintainability, Pythonic idioms) focuses the model's attention.

0%
Token Efficiency Gain
Refactored code maintains original functionality.
Refactored code demonstrates clear improvements in readability.
Refactored code demonstrates clear improvements in efficiency (if applicable to the given code).

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