What are the use cases for this prompt?

This prompt can be used for: Generating Python Code.

What is the difficulty level and which AI tool should I use?

This prompt is intermediate level. It works with all AI tools (ChatGPT, Claude, Gemini, etc.).

Are there variants of this prompt?

Yes, 3 variants are available: Beginner, Intermediate, Advanced.

💻DeveloppementIntermediateAll AIs

Sora Prompt for Generating Python Code

Sora, developed by OpenAI, is primarily known as a text-to-video generation model. However, within the OpenAI ecosystem, prompt engineering techniques applied to Sora can be adapted to structure clear and precise requests, including for generating Python code through associated conversational interfaces. Mastering the art of prompting with OpenAI tools allows obtaining functional, well-structured, and documented Python code. The main challenge lies in formulating detailed enough instructions so that the model understands the technical context, project constraints, and expected complexity level. A well-crafted prompt for generating Python code should include the target language, desired programming paradigm, libraries to use, and expected output format. In this guide, we offer optimized and tested prompts to get professional-quality Python code, whether you are a beginner looking to learn or an experienced developer wanting to speed up your development workflow.

Paste in your AI

Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.

You are a senior Python developer with 10 years of experience. Generate complete and functional Python code for [YOUR_FEATURE_DESCRIPTION]. The code must follow PEP 8 conventions, include type hints, Google-style docstrings, and robust error handling with custom exceptions. Structure the code into modular classes and functions. Add explanatory comments for complex parts. Include an if name == 'main' block with a concrete usage example. Prioritize standard Python 3.11+ libraries. If external dependencies are necessary, list them in a header comment with recommended versions.

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Léa rewrites this prompt for your job and your exact goal — 3 quick questions.

Why this prompt works

This prompt works by assigning an expert role that guides the model towards professional-level responses. Explicit technical constraints (PEP 8, type hints, docstrings) eliminate ambiguity and ensure code that meets industry standards. The request for modular structure and usage examples forces the model to produce immediately usable code rather than a simple snippet.

Use Cases

Generating Python Code

Variants

Expected Output

You will get a complete Python script, properly indented and documented, ready to run in your development environment. The code will include a modular architecture with separation of concerns, proper error handling, and a functional execution example in the main block.

Frequently Asked Questions

Can Sora actually generate quality Python code?

Sora is primarily a video generation model from OpenAI. For Python code generation, models from the GPT family (like GPT-4) are better suited. However, the prompt engineering techniques presented here are universal and work with all of OpenAI's conversational tools. What matters is the structure and precision of your prompt, regardless of which model you use.

How can I get error-free Python code on the first prompt?

To maximize the quality of the generated code, be as specific as possible in your prompt: specify the Python version, allowed libraries, output format, and edge cases to handle. Always include an example of the expected input/output. The richer your context, the more accurate and functional the generated code will be from the very first iteration.

What are the limitations of AI-generated Python code?

AI models can generate syntactically correct but logically flawed code, especially for complex algorithms or specific business cases. It's essential to always review, test, and validate the generated code. The main limitations involve complex state management, advanced performance optimizations, and knowledge of the most recent APIs. Use the generated code as a solid starting point, then refine it according to your needs.

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Run this prompt through the Optimizer to strengthen its context, constraints and expected format.

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Comments

LéaAI
Ajoute le contexte (CLI, API, script) et demande des tests unitaires : le code sera plus fiable et prêt à l'emploi. Précise aussi le framework si besoin (FastAPI, Flask, etc.).