Retrieval-Augmented Generation (RAG)
RAG is a technique that lets a large language model answer from your own documents: it retrieves the relevant passages from a knowledge base and gives them to the model as context, so answers are grounded in your data rather than the model's general training.
RAG addresses the biggest weakness of LLMs in business use — they don't know your specific information and can hallucinate. By first searching a trusted source (manuals, procedures, maintenance records) and feeding the top results to the model, RAG produces answers grounded in and citable to your own documents. It is the standard way to build a reliable internal assistant over industrial knowledge.
Related terms
Large Language Model (LLM) · Generative AI · Machine Learning (Industrial)
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