This article argues that using large language models (LLMs) for code generation or assistance creates an "ownership" problem because the outputs are not version-controlled like traditional code. It debunks several myths, including the idea that the same prompt always yields the same output, that re-running a free generation is equivalent to retrieving a stored version, and that LLM context is the same as project memory. The author proposes treating prompts as inputs, not lockfiles, and emphasizes the need for explicit provenance tracking, such as saving outputs with hashes and storing code changes as patches, to ensure accountability and reproducibility in software development. AI
IMPACT Highlights the need for better provenance and ownership tracking for LLM-generated code to prevent issues in production environments.
RANK_REASON The item is an opinion piece discussing best practices for using LLMs in software development, rather than a release or research paper.
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