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Prompt Engineering Guide

Mastering Code refactoring
on GPT-4o-mini

Stop guessing. See how professional prompt engineering transforms GPT-4o-mini's output for specific technical tasks.

The "Vibe" Prompt

"Refactor this Python code for better readability and performance: ```python def process_data(data_list): result = [] for item in data_list: if 'value' in item: val = item['value'] if isinstance(val, (int, float)): if val > 10: result.append(val * 2) else: result.append(val + 5) elif isinstance(val, str): result.append(val.upper()) return result ```"
Low specificity, inconsistent output

Optimized Version

STABLE
You are an expert Python developer with a focus on writing clean, efficient, and maintainable code. Your task is to refactor the provided Python code snippet. Follow these steps meticulously: 1. **Understand Current Functionality:** Briefly describe, in one or two sentences, what the original code does. 2. **Identify Areas for Improvement:** Pinpoint specific sections or patterns that can be refactored. Consider: * Readability and clarity (e.g., variable names, nested conditionals). * Performance (e.g., unnecessary iterations, redundant computations). * Pythonic idioms (e.g., list comprehensions, `get()` method). * Modularity and reusability. 3. **Propose Refactoring Strategy:** Outline the high-level changes you plan to make. For example, "Introduce helper function," "Replace loop with list comprehension," "Use `dict.get()`." Briefly justify why each strategy improves the code. 4. **Implement Refactored Code:** Provide the complete, refactored Python code block. 5. **Explain Changes:** For each major refactoring decision (e.g., new helper function, list comprehension), explain why it was made and how it improves the code. Here is the code to refactor: ```python def process_data(data_list): result = [] for item in data_list: if 'value' in item: val = item['value'] if isinstance(val, (int, float)): if val > 10: result.append(val * 2) else: result.append(val + 5) elif isinstance(val, str): result.append(val.upper()) return result ```
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt uses a chain-of-thought approach by breaking down the refactoring task into distinct, sequential steps. This forces the model to first understand the code, then critically evaluate it, propose a strategy, implement it, and finally justify its choices. This structured approach guides the model towards a more thoughtful and comprehensive refactoring, addressing not just syntax but also design principles. By explicitly asking for explanations and justifications, it encourages deeper reasoning and reduces the likelihood of superficial changes. The role-playing ('expert Python developer') also primes the model for a higher quality output.

0%
Token Efficiency Gain
The 'optimized_prompt' will produce a more detailed and logically sound refactored output.
The 'optimized_prompt' will encourage the model to explain 'why' certain changes were made, which is crucial for code review and learning.
The 'optimized_prompt' will reduce the chances of the model hallucinating or making irrelevant changes by focusing its reasoning.

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