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Prompt Engineering Guide

Mastering Code refactoring
on Llama 3.1 70B

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

The "Vibe" Prompt

"Refactor this Python code for better readability and performance: ```python def process_data(data): # Initial crude implementation processed_items = [] for item in data: if item['status'] == 'active': value = item['value'] * 2 temp_dict = {'id': item['id'], 'processed_value': value} processed_items.append(temp_dict) return processed_items ```"
Low specificity, inconsistent output

Optimized Version

STABLE
You are an expert Python software engineer specializing in clean, efficient, and maintainable code. Your task is to refactor the provided Python function. Follow these steps meticulously: 1. **Analyze Current Code:** Identify potential areas for improvement regarding: * Readability (e.g., list comprehensions, meaningful variable names) * Efficiency (e.g., avoiding unnecessary loops, better data structures) * Pythonic style (e.g., idiomatic constructs) 2. **Propose Refactoring Strategy:** Briefly outline the changes you intend to make and why. 3. **Implement Refactored Code:** Provide the complete, refactored Python function. 4. **Justify Changes:** Explain the benefits of your refactoring decisions, specifically addressing readability, performance, and adherence to Python best practices. Here is the code to refactor: ```python def process_data(data): # Initial crude implementation processed_items = [] for item in data: if item['status'] == 'active': value = item['value'] * 2 temp_dict = {'id': item['id'], 'processed_value': value} processed_items.append(temp_dict) return processed_items ``` Ensure the refactored code maintains identical functionality.
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages several powerful techniques for Llama 3.1 70B: 1. **Role-Playing:** Assigning the persona 'expert Python software engineer' primes the model for a high-quality, professional output. 2. **Chain-of-Thought (CoT):** Breaking down the task into sequential, explicit steps (Analyze, Propose, Implement, Justify) forces the model to think through the problem systematically. This significantly improves the logical coherence and quality of the refactoring. 3. **Clear Objectives & Constraints:** Explicitly stating requirements like 'clean, efficient, and maintainable code' and 'identical functionality' guides the model towards the desired outcome. 4. **Specific Improvement Areas:** Highlighting 'Readability', 'Efficiency', and 'Pythonic style' gives the model concrete criteria to evaluate and optimize against. 5. **Structured Output Request:** Although not explicitly for output format, the structured steps encourage a structured thought process, leading to a more organized and comprehensive response. 6. **Reduced Ambiguity:** The naive prompt is highly ambiguous. 'Better readability and performance' is subjective. The optimized prompt provides actionable sub-goals.

0%
Token Efficiency Gain
The refactored code should use a list comprehension for conciseness.
The justification should explain the performance benefits of avoiding explicit loops if applicable (though for this specific case, list comprehension is more about readability/conciseness).
The justification should highlight improved readability due to the Pythonic style.

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