In most industrial organizations, essential knowledge is scattered across instructions, regulations, standards, specifications and past reports. Finding and reconciling this information takes time and professional judgement.
Retrieval-augmented generation (RAG) gives a generative model access to retrieved documents when composing an answer. Unlike an unconstrained model conversation, such a system can be designed to provide citations to the underlying sources.
Citations alone do not make an answer correct. A reliable expert system requires document version control, access permissions, currency checks, test questions and a rule for declining to answer when the evidence is insufficient.
Industrial operations add further requirements: enterprise access controls, deployment in a protected environment, change logs, provenance checks and a way for subject-matter experts to correct errors.
Decision-support systems may use RAG as one component, combining fact retrieval with calculations, business rules, risk models and approved workflows. Responsibility for decisions must remain clearly assigned between technology and authorized personnel.
Measure outcomes such as time spent searching, answer quality on a reference question set, correctness of citations, escalation rates and actual labour savings. These measures allow organizations to compare solutions without promising universal accuracy.