This article argues that prompts used with large language models should be treated as infrastructure rather than conversational inputs. Treating prompts as infrastructure means versioning them in systems like Git, reviewing them through pull requests, and ensuring they are narrow and reusable. This approach helps mitigate the risk of silent omissions, where a model fails to follow an instruction without any indication, which is a significant failure mode for long, conversational prompts. By making prompts specific, testable, and composable, teams can build more reliable and maintainable AI-powered products. AI
IMPACT Advocates for treating LLM prompts as version-controlled infrastructure to improve reliability and maintainability in AI product development.
RANK_REASON Article discusses best practices for prompt engineering and LLM usage, framing it as a commentary on software development principles applied to AI.
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