Six questions before you add an LLM (cameronmpalmer.medium.com)

A recent article highlights the complexities of integrating large language models (LLMs) into workflow systems, urging businesses to critically analyze their needs before implementation. The author, an AI implementation consultant, shares a personal experience where an LLM-driven prospect discovery system created inefficiencies due to excessive flexibility, leading to irrelevant and duplicated entries. This underscores the risk of mindless AI adoption driven by metrics rather than problem-solving orientation. The significance of this discussion lies in its systematic approach to evaluating LLM applicability. The author emphasizes six key questions to assess whether an LLM is appropriate for a task, including the need for deterministic outputs, the ability to specify workflows in advance, and the cost-effectiveness of verification processes. By prioritizing a clear understanding of specific challenges over the impulse to implement AI for the sake of innovation, organizations can better leverage the strengths of LLMs while mitigating their inherent limitations in determinism and output consistency. This framework encourages a balanced view that combines the adaptability of LLMs with the reliability of traditional coding solutions for optimal performance.