Skip to main content
Back to Library
Prompt Engineering Guide

Mastering Debug code
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

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

Optimized Version

STABLE
You are a highly experienced Python developer with a deep understanding of common errors, best practices, and debugging methodologies. Your task is to meticulously analyze the provided Python code, identify any syntactic, logical, or runtime errors, and then provide a corrected, fully functional version of the code. **Thought Process:** 1. **Understand the Goal:** Briefly describe what you perceive the code is attempting to achieve. 2. **Initial Scan & Syntax Check:** Perform a quick scan for obvious syntax errors, typos, or indentation issues. 3. **Logical Flow Analysis (Line-by-Line):** * Examine each function, loop, and conditional statement. * Identify potential off-by-one errors, incorrect comparisons, or misapplied algorithms. * Trace variable values through critical sections. 4. **Error Identification & Explanation:** For each identified error, explain: * What the error is. * Why it's an error (e.g., incorrect logic, missing import, type mismatch). * The potential impact of the error. 5. **Correction Strategy:** Outline your approach to fix each identified issue. 6. **Revised Code:** Present the complete, corrected Python code. 7. **Testing & Verification (Mental Walkthrough):** Briefly explain how a user could verify the fix or what edge cases were considered. **Original Code:** ```python [CODE] ``` **Let's begin. Thought Process:**
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages chain-of-thought by explicitly asking the model to think step-by-step. It establishes a persona ('highly experienced Python developer'), defines clear debugging stages (understanding, scanning, flow analysis, error identification, correction, verification), and uses structured markdown to separate different parts of the thought process and output. This guides the model to perform a more thorough and systematic analysis rather than a superficial fix. The explicit instruction to 'trace variable values' and 'outline your approach to fix' encourages deeper reasoning. The 'Revised Code' section is clearly demarcated, making the output easy to parse. This structured approach forces the model to articulate its reasoning, leading to more accurate and robust debugging.

0%
Token Efficiency Gain
The optimized prompt explicitly asks for a 'revised, fully functional code'.
The optimized prompt requests a 'thought process' with specific steps: 'Understand the Goal', 'Initial Scan & Syntax Check', 'Logical Flow Analysis', 'Error Identification & Explanation', 'Correction Strategy', 'Revised Code', 'Testing & Verification'.
The optimized prompt establishes a persona: 'You are a highly experienced Python developer'.

Ready to stop burning tokens?

Join 5,000+ developers using Prompt Optimizer to slash costs and boost LLM reliability.

Optimize My Prompts