RAG is an AI technique that connects Large Language Models (LLMs) to a private, external knowledge base. It retrieves relevant facts from your own data before generating an answer, ensuring the response is accurate, up-to-date, and trustworthy.

RAG primarily solves “AI hallucinations”, the tendency for LLMs to invent facts. Grounding the AI in your company’s verified data ensures the answers it provides are factually correct and relevant to your business.

RAG gives an LLM access to current information for fact-based answers, like an open-book test. Fine-tuning retrains the model’s core behavior to change its style or skills. For tasks requiring factual accuracy, RAG is typically faster and more cost-effective.

The main benefits are dramatically improved accuracy, enhanced user trust through verifiable sources, lower costs by avoiding constant model retraining, and robust data security by keeping your proprietary information in a private system.

Common applications include advanced customer support chatbots, internal knowledge search tools for employees, secure AI assistants for regulated industries like finance and healthcare, and personalized e-commerce recommendation engines.

First, your company’s data is indexed into a searchable vector database. When a user asks a question, the system retrieves the most relevant information from that database and provides it to the LLM, which then generates a precise, evidence-based answer.