The author argues that using Large Language Models (LLMs) directly for scheduled recurring analysis is problematic due to their inherent inconsistency. When an LLM generates analysis prompts, the results can vary even with the same input, making it impossible to distinguish between genuine changes in data and changes caused by the LLM's output variations. To address this, the author proposes a phased approach where LLMs are used during the 'build time' for exploration and code generation, followed by human review and version control of the generated code. This codified metric definition is then executed deterministically by a scheduler, with LLMs used again downstream for interpretation. AI
IMPACT Suggests a framework for more reliable LLM integration in data analysis workflows.
RANK_REASON Opinion piece discussing the limitations of LLMs for scheduled analysis tasks.
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