Retrieval-augmented generation (RAG) is an AI technique that grounds a large language model’s answers in specific documents retrieved at query time, rather than relying only on what the model learned during training. When a user asks a question, the system first retrieves the most relevant passages from a connected knowledge base, then the model generates an answer based on those passages — and cites them.
Why RAG matters for industry
In maintenance and operations, a wrong answer can be a safety or compliance risk. RAG makes AI usable in these settings because:
- Answers are grounded in source documents — manuals, SOPs, service records — not the model’s general training data.
- Every claim can be cited back to a page, section or video timestamp, so technicians can verify it.
- Hallucinations are minimized — if the answer is not supported by the connected content, the system does not invent one.
- Knowledge stays current — updating the documents updates the answers, with no model retraining required.
RAG in practice
An industrial AI copilot uses RAG to turn a plant’s scattered documentation into instant, trustworthy guidance. VSight Nova applies this approach so every answer links back to the exact source it came from.