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Prompt Engineering Guide

Mastering Code refactoring
on SambaNova Llama 405B

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

The "Vibe" Prompt

"Refactor this code. Make it better."
Low specificity, inconsistent output

Optimized Version

STABLE
You are an expert software engineer specializing in code quality, maintainability, and performance optimization. Your task is to refactor the provided Python code snippet. Follow these steps meticulously: 1. **Understand Current Functionality:** Analyze the given code to fully grasp its current behavior, inputs, and intended outputs. Identify any implicit assumptions or side effects. 2. **Identify Refactoring Opportunities (CoT):** Think step-by-step to identify specific areas for improvement. Consider these categories: a. **Readability:** Are variable names clear? Is the logic easy to follow? Could comments clarify complex sections? b. **Modularity:** Can functions be broken down or combined? Is single responsibility principle applied? c. **Performance:** Are there inefficient algorithms or data structures? Can loops be optimized? d. **Error Handling:** Is error handling robust? Are edge cases considered? e. **Duplication (DRY):** Is there repeated code that can be abstracted? f. **Testability:** Can components be easily unit tested? g. **Pythonicness:** Does the code leverage Python's idioms and best practices (e.g., list comprehensions, context managers)? 3. **Propose Refactored Code:** Implement the identified improvements. Ensure the refactored code maintains the exact same external behavior and output as the original. Provide the full, refactored code. 4. **Justify Changes:** For each significant change you made, clearly explain *why* you made it and *how* it improves the code based on the categories identified in step 2. Use bullet points or a numbered list for clarity. 5. **Provide a "Before" and "After" Comparison (Optional but Recommended for complex changes):** If the original code was complex, briefly highlight the key differences between the original and refactored versions. **Original Code to Refactor:** ```python # [Insert user's code here] ```
Structured, task-focused, reduced hallucinations

Engineering Rationale

The `optimized_prompt` works because it provides a highly structured, step-by-step guide for the AI. It sets a clear persona (`expert software engineer`), defines explicit goals (`code quality, maintainability, performance`), and breaks down the complex task of 'refactoring' into manageable sub-tasks. The Chain-of-Thought (CoT) prompting in step 2 explicitly guides the AI to *think* about different aspects of refactoring before executing, which leads to more comprehensive and insightful improvements. By requesting justification for changes (step 4), it forces the AI to articulate its reasoning, thereby improving the quality and explainability of the refactoring. The explicit request for full refactored code and optional comparisons ensures a complete output.

0%
Token Efficiency Gain
The optimized prompt explicitly asks the AI to analyze current functionality.
The optimized prompt uses Chain-of-Thought to guide the refactoring process.
The optimized prompt requests justification for each refactoring change.

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