Large language models (LLMs) have made strides in content generation but often fall short in business efficiency due to their limited training data. This limits their effectiveness in enterprise-specific environments. Retrieval-augmented generation (RAG) addresses this by allowing LLMs to access external data, such as a company’s knowledge base, before generating responses, with citations included. This enables more accurate, relevant outputs without extensive fine-tuning. In customer service, for example, RAG-powered chatbots can provide specific, up-to-date responses by leveraging an enterprise’s data, enhancing relevance and accuracy in critical applications like knowledge management and domain-specific copilots.

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