This article advocates for treating AI prompts and evaluation datasets as critical software artifacts that require rigorous version control and review processes, similar to traditional code. It argues that pinning model configurations and prompts directly in code or ad-hoc text files leads to unmanageable drift and debugging challenges. The proposed workflow involves storing prompts in a version-controlled repository or registry, maintaining golden evaluation datasets alongside prompts, and managing model configurations as code, ensuring that changes are auditable, reviewable, and easily rolled back. AI
IMPACT Adopting prompt versioning and rigorous evals can improve AI feature reliability and reduce operational overhead.
RANK_REASON The article describes a workflow and tooling for managing AI prompts and evaluations, positioning it as a best practice for software development.
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