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Prompt Engineering Guide

Mastering Debug code
on SambaNova Llama 405B

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

The "Vibe" Prompt

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

Optimized Version

STABLE
You are an expert software engineer specializing in [SPECIFIC_LANGUAGE_OR_FRAMEWORK, e.g., Python and Django] and experienced at debugging complex issues effectively and efficiently. Your task is to analyze the provided code snippet, identify any bugs, inefficiencies, or potential improvements, and then offer a corrected, optimized version. For each change, clearly explain the 'Reasoning' behind it based on best practices, common pitfalls, and the code's intended functionality. Before providing the corrected code, first perform a step-by-step thinking process. 1. **Understand the Goal**: What is this code *supposed* to achieve? Identify the core requirements. 2. **Initial Scan for Obvious Issues**: Look for syntax errors, common logical flaws (e.g., off-by-one, incorrect loop conditions), and Anti-patterns. 3. **Data Flow Analysis**: Trace how data moves through the code. Are variables initialized correctly? Are values modified as expected? 4. **Edge Case Consideration**: What happens with empty inputs, nulls, very large inputs, or specific boundary conditions? 5. **Security/Performance Review**: Are there any glaring security vulnerabilities or performance bottlenecks? 6. **Formulate Corrections**: Based on the analysis, design the necessary fixes and improvements. 7. **Verify Changes (Mental Walkthrough)**: Mentally execute the corrected code with test cases, including the original problematic scenario, to ensure the fix works and doesn't introduce new issues. [PASTE_CODE_HERE] After your thinking process, present the 'Corrected Code' and for each modification, provide a 'Reasoning' explaining the fix. Do not include any conversational filler outside of the specified format.
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages chain-of-thought reasoning, guiding the model through a structured debugging process. It explicitly sets the persona ('expert software engineer'), clarifies the task, and breaks down the debugging into incremental, logical steps. This forces the model to deeply analyze the code rather than just surface-level pattern matching. By asking for 'Reasoning' for each change, it ensures the model provides comprehensive explanations, demonstrating understanding. Specifying '[SPECIFIC_LANGUAGE_OR_FRAMEWORK]' further narrows the context, improving accuracy, and 'Do not include any conversational filler' reduces unnecessary token generation.

25%
Token Efficiency Gain
The output for the optimized prompt provides a step-by-step thinking process before presenting the corrected code and reasoning.
The optimized prompt's corrected code includes specific reasoning for each modification.
The optimized prompt's output focuses solely on the code and reasoning, without conversational filler.

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