What Is RAG? Why Retrieval-Augmented Generation Is the Key to Trustworthy Enterprise AI

Standard AI models, trained on data up to a certain date, have two limitations that matter for enterprise use. First, they cannot access information that was created after their training cutoff. A model trained on data up to April 2024 does not know about events, regulations, or products that appeared after that date. Second, they do not have access to your organisation's proprietary information: your policies, your products, your internal reports, your customer data.

These limitations mean that a standard AI model answering business questions is drawing on general public knowledge and may be out of date. For many business tasks, this is insufficient and potentially dangerous: an AI answering questions about your regulatory obligations should know your specific regulatory context, not a generic version of the rules.

RAG addresses this by adding a retrieval step before the AI generates a response. Rather than the AI drawing only on its training data, a RAG system first searches a knowledge base of documents (your policies, your product information, your regulatory guidance, your internal reports) for content relevant to the query. The AI is then instructed to generate its response based on the retrieved documents, rather than on its training data alone.

The result is an AI that can tell you what your specific policy says, not what a generic policy in your sector might typically say. It can answer questions about your current products accurately, because it is working from your current product documentation. It can apply current regulatory guidance, because the guidance has been added to the knowledge base.

The key governance benefit is that RAG responses can be attributed to source documents. Rather than an AI making a claim that cannot be verified, a RAG system can say 'based on your AI policy document from March 2025, the answer is...' This attribution dramatically reduces hallucination risk and enables verification.

Microsoft Copilot uses RAG extensively. When you use Copilot in your Microsoft 365 environment, it retrieves relevant documents from your SharePoint, emails, and Teams conversations to ground its responses in your organisation's actual content. This is why the quality of your Microsoft 365 data hygiene and document organisation has such a significant impact on the quality of Copilot outputs.

Azure AI Search is Microsoft's enterprise RAG infrastructure: a platform for building knowledge bases that AI systems can query to ground their responses in your organisation's information. Organisations building custom AI applications on Azure typically use Azure AI Search as the retrieval component of a RAG architecture.

For boards and governance teams, RAG is important because it creates a more governable form of AI than standard model querying. When AI is grounded in specific documents, you can control what knowledge it draws on, you can audit what information it has access to, and you can update the knowledge base when information changes.

This does not mean RAG eliminates all AI risk. The AI still generates the response using language model inference, which means it can still make errors in summarising or applying the retrieved information. But the errors become easier to identify (because the source document can be checked) and the AI's knowledge base is easier to govern (because it is explicit and controlled rather than embedded in opaque model weights).