RAG — Retrieval-Augmented Generation — is the architecture behind most enterprise AI applications that work on company-specific data. If your organisation is building an AI tool that answers questions about internal documents, policies, or knowledge bases, it is almost certainly using RAG. Here is what it is and why it matters.
The problem RAG solves
Large language models are trained on general data — the internet, books, code. They do not know anything about your company's specific processes, your internal documents, your product specifications, or anything else that is not in their training data.
You could try to fix this by fine-tuning the model on your data — retraining it to incorporate your specific knowledge. But fine-tuning is expensive, slow, and has to be redone every time your data changes. For dynamic enterprise knowledge bases, it is impractical.
RAG solves this differently: at query time, the system retrieves the relevant documents from your knowledge base and provides them to the model as context. The model then generates an answer based on both its general training and the specific documents you provided.
How it works in practice
When a user asks a question, the RAG system first searches a vector database containing your documents — this search is semantic rather than keyword-based, meaning it finds conceptually relevant documents rather than just documents containing the exact search terms.
The relevant documents are then inserted into the prompt alongside the user's question. The model sees: "Here are some relevant documents from your knowledge base [documents]. Given these, please answer: [question]." Because the model's answer is grounded in the retrieved documents, it is much more likely to be accurate and specific to your context.
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Why enterprises use it
RAG produces AI systems that can answer questions about company-specific content without the cost and complexity of fine-tuning. It is also more transparent — you can see which documents were retrieved, which makes it easier to understand why the model gave a particular answer and to verify its accuracy.
For L&D teams, understanding RAG is valuable both for evaluating AI tool proposals and for designing AI literacy programmes that reflect how enterprise AI systems actually work.
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