Planting flags in AI coding territory

10 months ago

Large Language Models (LLMs) can write and review code but offer no guarantees about correctness or efficiency.
Key questions when using LLMs include having workable requirements, meaningful tests, and understanding code to fix failures.
AI coding spans a spectrum from prototyping to production, balancing autonomy and human oversight.
The importance of overseeing AI-generated code depends on project risks and requirements.
LLMs can assist in prototyping, helping to clarify ideas before committing to a full project.
Testing AI-generated code requires meaningful assertions, not just boilerplate, to ensure reliability.
Deleting or preventing unnecessary code is a crucial skill, often requiring human judgment.
Human oversight remains essential in deciding what to test and why, even with AI assistance.

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