A short, non-technical explainer written for a general business audience rather than AI researchers. Using plain analogies (comparing RAG to a student who's allowed to bring reference notes into an open-book exam, versus one who must answer from memory alone), the article explains why large language models can be confidently wrong, how connecting a model to an external, trusted, and up-to-date knowledge source through retrieval fixes that, and why this approach is cheaper and faster to keep current than retraining a model. It avoids technical jargon almost entirely and focuses on the business case: accuracy, trust, and source citation.
This is the ideal first read for a process safety engineer with no AI background, before tackling the more technical papers in this list. It builds the core intuition — that RAG grounds AI answers in your own trusted documents (PHA reports, incident investigations, procedures) rather than the model's general training data — which is exactly the trust and traceability concern process safety professionals will rightly raise about any AI knowledge tool.
