This article proposes treating prompts as code within an LLMOps architecture. It argues that prompts, like software, should be version-controlled, tested, and managed through a structured development lifecycle. This approach aims to bring software engineering best practices to the management and deployment of large language models. AI
IMPACT Suggests a framework for more robust and scalable management of LLM prompts, akin to software development.
RANK_REASON Article discusses a conceptual approach to managing LLM prompts, not a new release or product.
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