What makes this Mistral Large 2 prompt unique?

This prompt has been engineered using Bayesian optimization to specifically improve Debug code performance. It achieves a 0% reduction in token usage compared to naive prompts.

Does this prompt work with other models besides Mistral Large 2?

While highly optimized for Mistral Large 2, the underlying structural patterns (identity enforcement, task delimitation) are highly effective across most frontier models including GPT-4 and Llama 3.

How do I use this optimized template?

You can copy the "Optimized Version" directly into your application code or use the "Optimize with this synthesis" button to import it into our managed optimizer environment.

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Prompt Engineering Guide

Mastering Debug code
on Mistral Large 2

Stop guessing. See how professional prompt engineering transforms Mistral Large 2's output for specific technical tasks.

The "Vibe" Prompt

"Debug this code: ```python import pandas as pd def process_data(file_path): df = pd.read_csv(file_path) df['new_col'] = df['col1'] + df['col2'] return df # Example usage # result = process_data('data.csv') # print(result.head()) # PROBLEM: This code sometimes fails with a TypeError when 'col1' or 'col2' contain non-numeric data. ```"

Low specificity, inconsistent output

Optimized Version

STABLE

```json { "task": "Code Debugging", "model_target": "Mistral Large 2", "problem_description": "The provided Python function `process_data` uses pandas to read a CSV, then attempts to sum `col1` and `col2` into a new column. It consistently fails with a `TypeError` when either `col1` or `col2` contain non-numeric data, such as strings or NaNs that prevent arithmetic operations.", "code_to_debug": """import pandas as pd def process_data(file_path): df = pd.read_csv(file_path) df['new_col'] = df['col1'] + df['col2'] return df""", "chain_of_thought_steps": [ "1. **Understand the Error Type:** The `TypeError` indicates an operation on incompatible data types. In pandas, adding columns typically requires numeric types.", "2. **Identify Problematic Operation:** The line `df['new_col'] = df['col1'] + df['col2']` is the direct cause of the error. Addition operation is failing.", "3. **Hypothesize Root Cause:** The problem description explicitly states 'non-numeric data' in `col1` or `col2`. This would lead to `TypeError` during addition.", "4. **Brainstorm Solutions for Type Handling:** a. **Coercion to Numeric:** Convert columns to numeric, handling errors by setting them to `NaN`. b. **Error Handling/Skipping:** Catch the error or skip non-numeric rows (less ideal for data integrity). c. **Validation:** Check types before operation and raise a more descriptive error.", "5. **Select Best Solution:** Coercion (`pd.to_numeric`) is often the most robust solution for this scenario, allowing the operation to proceed while gracefully handling non-numeric entries by making them `NaN`, which can then be further handled (e.g., filled or dropped). * Consider `errors='coerce'` for `pd.to_numeric` to turn invalid parsing into `NaN`.", "6. **Formulate Corrected Code:** Apply the chosen solution to the code." ], "requested_output": "Provide the corrected Python code, an explanation of the fix, and a brief example demonstrating its robustness against mixed data types." } ```

Structured, task-focused, reduced hallucinations

Engineering Rationale

The optimized prompt leverages a structured JSON format and a detailed chain-of-thought process. It explicitly defines the task, problem, and the exact code, avoiding ambiguity. The chain-of-thought guides the model through understanding the error, identifying the cause, brainstorming solutions, and selecting the most appropriate fix, leading to a more accurate and robust solution. This structured approach mimics human expert debugging, fostering a deeper reasoning process in the LLM.

Token Efficiency Gain

The model should correctly identify `pd.to_numeric(..., errors='coerce')` as the primary solution.

The corrected code should include the type conversion before the addition operation.

The explanation should clearly articulate *why* the original code failed and *how* the fix addresses it.

How We Validate This Prompt

Every optimized prompt for Debug code on Mistral Large 2 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 should correctly identify `pd.to_numeric(..., errors='coerce')` as the primary solution.
The corrected code should include the type conversion before the addition operation.
The explanation should clearly articulate *why* the original code failed and *how* the fix addresses it.
The example should demonstrate input data with mixed types and show the robust output.

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Mastering Debug code on Mistral Large 2