Retrieval-Augmented Generation (RAG) finds relevant passages in your own documents first, then composes an answer grounded in them — which makes cited, checkable answers possible.
Provide work context
Manuals and work history
Generate using retrieved context
Review the original documents
Retrieval-Augmented Generation (RAG) finds relevant passages in your own documents first, then composes an answer grounded in them — which makes cited, checkable answers possible.
A language model answering alone draws on the general knowledge it was trained on. Your equipment manuals and work standards are not in there, so it can produce answers that sound right and are wrong.
RAG solves this by inserting a retrieval step before generation. Internal documents are prepared in a searchable form; when a question arrives, the relevant passages are retrieved and the answer is written from them. Which document and which passage was used can be shown alongside.
This matters in manufacturing because knowledge is scattered across documents and people. Equipment manuals, work standards and past alarm responses live in different places and cannot be found when needed.
Fine-tuning retrains the model on your data; RAG leaves the model alone and supplies reference material. Where documents change often, RAG is the easier fit — you update the documents and skip retraining entirely.
Terms that make more sense read together.
What IYULAB provides in this area.
Questions that come up when evaluating RAG.
RAG does not retrain a model, so no training dataset is required. It starts with organising the documents you already have into a searchable form.
It can be run on premises so nothing leaves. In manufacturing this is frequently a hard requirement.
It reflects whatever wording appears in your documents. Linking synonymous terms benefits from a glossary being in place.
A look at your equipment and current workflow is usually enough to scope it.
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