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Prompt Engineering Guide

Mastering Debug code
on DeepSeek V3

Stop guessing. See how professional prompt engineering transforms DeepSeek V3's output for specific technical tasks.

The "Vibe" Prompt

"Hey, can you debug this Python code for me? It's giving me an error. Here's the code: ```python import pandas as pd def process_data(file_path): df = pd.read_csv(file_path) df['new_col'] = df['existing_col'] * 2 return df # Example usage # data = process_data('my_data.csv') # print(data.head()) ``` I'm getting a KeyError."
Low specificity, inconsistent output

Optimized Version

STABLE
TASK: DEBUG CODE SNIPPET. CONTEXT: - Programming Language: Python 3.x - Libraries/Frameworks: Pandas - Error Type: KeyError - Expected Behavior: User expects `process_data` to read a CSV, create a new column, and return a DataFrame. CODE: ```python import pandas as pd def process_data(file_path): # The error likely occurs when accessing 'existing_col' df = pd.read_csv(file_path) df['new_col'] = df['existing_col'] * 2 return df # Example usage (commented out, but relevant for context) # data = process_data('my_data.csv') # print(data.head()) ``` DEBUGGING STEPS (CHAIN-OF-THOUGHT): 1. ANALYZE ERROR: A 'KeyError' in Pandas typically means a column name does not exist. 2. LOCATE ERROR SOURCE: The line `df['new_col'] = df['existing_col'] * 2` is the likely culprit, specifically `df['existing_col']`. 3. FORMULATE HYPOTHESIS: The column 'existing_col' does not exist in the DataFrame loaded from `file_path`. 4. PROPOSE SOLUTION: a. Check `df.columns` to verify available columns after `pd.read_csv`. b. If 'existing_col' is missing or misspelled, correct the column name or handle its absence. c. Consider adding error handling (e.g., `try-except KeyError`) or a check (`if 'existing_col' in df.columns:`) for robustness. REQUIRED OUTPUT: Provide the corrected code and a concise explanation of the fix. ```python # Corrected code will go here, after debugging. ```
Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt works better for DeepSeek V3 because it provides a highly structured input that aligns with how large language models process information. 1. **Explicit Task Definition**: 'TASK: DEBUG CODE SNIPPET' immediately sets the model's objective. 2. **Detailed Context**: Specifies the language, libraries, error type, and expected behavior. This gives the model crucial background information. 3. **Clear Code Block**: The code is presented unambiguously. 4. **Chain-of-Thought (CoT)**: The 'DEBUGGING STEPS' section guides the model through a logical problem-solving process. This encourages systematic reasoning, reduces hallucination, and helps the model arrive at the correct solution more reliably. It effectively 'thinks aloud' for the model. 5. **Pre-computation/Pre-analysis**: By outlining the common cause of KeyError in Pandas and pointing to the exact line, the prompt prunes the search space for the model, making its task easier and more efficient. 6. **Desired Output Format**: 'REQUIRED OUTPUT: Provide the corrected code and a concise explanation of the fix.' ensures the model's response is exactly what's needed.

0%
Token Efficiency Gain
The model correctly identifies 'existing_col' as the source of the KeyError.
The model suggests checking `df.columns` or verifying the column name.
The model proposes robust error handling or a pre-check for column existence.

How We Validate This Prompt

Every optimized prompt for Debug code on DeepSeek V3 is scored against a fixed set of evaluation assertions. A revision ships only when it passes all of them, so the 0% token reduction never comes at the cost of output quality.

  • The model correctly identifies 'existing_col' as the source of the KeyError.
  • The model suggests checking `df.columns` or verifying the column name.
  • The model proposes robust error handling or a pre-check for column existence.
  • The output adheres to the 'CORRECTED CODE' and 'CONCISE EXPLANATION' format.

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Run any prompt through the same optimizer that produced this DeepSeek V3 guide — clarity, structure, and token efficiency in one pass.

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