Asking Better Questions: A Brewer's Guide to Using Generative Artificial Intelligence Without Being Fooled by It

Abstract

Brewers are already meeting artificial intelligence (AI) tools at work, so the objective of this article is practical rather than polemical: to set out how a brewery can use today's general-purpose AI tools well, and how to recognize when their output should not be trusted. "Artificial intelligence" is an umbrella term; machine learning, deep learning, and the large language models (LLMs) behind today's chatbots nest inside it. That distinction matters, because the published brewing evidence sits almost entirely in the machine-learning branch rather than the generative one. The article reviews the brewing machine-learning literature, then draws on published evaluations of LLMs from clinical medicine, control engineering, engineering statics, and general science assessment to make a narrow claim: as things stand, these tools are strong as evidence assembly engines and weak as domain reasoners. They will draft documents, organize information, absorb repetitive administrative work, and help a brewer with no programming background build a working calculator or spreadsheet model. They are not reliable authorities for production decisions, corrective actions, safety, compliance, validated procedures, or product release. Three practical questions follow: what AI can help with, what it must never be used for, and how to check what it gives you. A verification checklist and a vague-versus-specific worked example support the third. The article concludes that these tools serve brewers at every career stage, because assembly work is checkable at any level, whereas diagnostic output is something a junior brewer escalates rather than acts on. Used this way, AI raises the value of brewing judgment rather than replacing it.