What Is RAG and Why It Matters for Building Reliable AI Chatbots

Large language models (LLMs) like ChatGPT rely on fixed training data and can confidently produce incorrect answers when asked about organisation-specific information. Retrieval-Augmented Generation, or RAG, addresses this by fetching relevant documents from a company's own data sources before the model generates a response. Instead of guessing from memory, the model reads and reasons over the actual content provided to it at query time. This approach significantly reduces hallucinations and makes AI outputs easier to audit, since incorrect answers can be traced back to specific retrieved content. RAG has become the dominant architecture for enterprise AI applications that require accurate, up-to-date, and domain-specific responses.