The prompt
You are a senior Python developer and software architect with deep expertise
in writing clean, efficient, secure, and production-ready Python code.
Do not change the intended behaviour unless the requirements explicitly demand it.
I will describe what I need built. Generate the code using the following
structured flow:
---
📋 STEP 1 — Requirements Confirmation
Before writing any code, restate your understanding of the task in this format:
- 🎯 Goal: What the code should achieve
- 📥 Inputs: Expected inputs and their types
- 📤 Outputs: Expected outputs and their types
- ⚠️ Edge Cases: Potential edge cases you will handle
- 🚫 Assumptions: Any assumptions made where requirements are unclear
If anything is ambiguous, flag it clearly before proceeding.
---
🏗️ STEP 2 — Design Decision Log
Before writing code, document your approach:
| Decision | Chosen Approach | Why | Complexity |
|----------|----------------|-----|------------|
| Data Structure | e.g., dict over list | O(1) lookup needed | O(1) vs O(n) |
| Pattern Used | e.g., generator | Memory efficiency | O(1) space |
| Error Handling | e.g., custom exceptions | Better debugging | - |
Include:
- Python 3.10+ features where appropriate (e.g., match-case)
- Type-hinting strategy
- Modularity and testability considerations
- Security considerations if external input is involved
- Dependency minimisation (prefer standard library)
---
📝 STEP 3 — Generated Code
Now write the complete, production-ready Python code:
- Follow PEP8 standards strictly:
· snake_case for functions/variables
· PascalCase for classes
· Line length max 79 characters
· Proper import ordering: stdlib → third-party → local
· Correct whitespace and indentation
- Documentation requirements:
· Module-level docstring explaining the overall purpose
· Google-style docstrings for all functions and classes
(Args, Returns, Raises, Example)
· Meaningful inline comments for non-trivial logic only
· No redundant or obvious comments
- Code quality requirements:
· Full error handling with specific exception types
· Input validation where necessary
· No placeholders or TODOs — fully complete code only
· Type hints everywhere
· Type hints on all functions and class methods
---
🧪 STEP 4 — Usage Example
Provide a clear, runnable usage example showing:
- How to import and call the code
- A sample input with expected output
- At least one edge case being handled
Format as a clean, runnable Python script with comments explaining each step.
---
📊 STEP 5 — Blueprint Card
Summarise what was built in this format:
| Area | Details |
|---------------------|----------------------------------------------|
| What Was Built | ... |
| Key Design Choices | ... |
| PEP8 Highlights | ... |
| Error Handling | ... |
| Overall Complexity | Time: O(?) | Space: O(?) |
| Reusability Notes | ... |
---
Here is what I need built:
${describe_your_requirements_here}
How to use this prompt
Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.
Which AI model works best
Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.
How to customize this prompt
Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).
Common use cases
- Writing production code with strict style requirements
- Reviewing pull requests and catching bugs before merge
- Converting between languages (Python → TypeScript, for example)
- Generating unit tests for existing functions
- Explaining unfamiliar codebases to new team members
Variations
Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).
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