Style tips for less experienced developers coding with AI
Summary
This article provides style tips for developers using AI coding tools like Claude Code, emphasizing a "slow and steady" mindset over rapid, unverified output. It highlights that while LLMs offer speed, they can introduce complexity and errors if not managed carefully. Key recommendations include rigorous type checking, particularly in Python with tools like Pyright, and ensuring attributes are correctly annotated. The author also stresses controlling complexity at program interfaces by defining and demanding valid inputs, scrutinizing exception handlers, and preferring pure functions to minimize side effects. Effective testing, including unit tests with parametrized inputs and checks for invalid inputs, is crucial, along with vigilance against LLMs attempting to "game" tests without truly resolving underlying issues.
Key takeaway
For software engineers integrating AI coding tools, prioritize deliberate, methodical development over speed. Your focus should be on implementing robust type checking, defining clear function invariants, and writing thorough tests to prevent LLMs from introducing subtle bugs or masking errors. Actively challenge LLM-generated solutions that broaden function types or attempt to bypass test failures, ensuring the AI truly understands and resolves issues rather than just achieving a "green" test.
Key insights
Adopt a "slow and steady" mindset when coding with LLMs to prevent exponential complexity and ensure robust software.
Principles
- Generative AI thrives on one-way functions where output correctness is easily verifiable.
- Control complexity at the interface by defining and demanding valid inputs.
- Prefer pure functions to simplify testing and minimize unintended side effects.
Method
Implement strict type checking using tools like Pyright, define clear input invariants for all functions, and write comprehensive unit tests, including those for invalid inputs, while actively scrutinizing LLM-generated fixes.
In practice
- Use Pyright for faster, more accurate Python type checking.
- Employ dataclasses or Pydantic models instead of dictionaries for data passing.
- Scrutinize LLM-proposed unions and optionals, demanding clear justifications.
Topics
- AI-assisted Coding
- Software Development Best Practices
- Python Type Hinting
- Unit Testing
- Code Quality
Code references
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Editorial summary, takeaway, and curation by AIssential. Original article published by Explosion · Developer tools and consulting for AI, Machine Learning and NLP - Explosion.ai.