Short answer
Retrieval-augmented generation (RAG) is a technique where an AI system first retrieves relevant information from trusted sources, such as a knowledge base, policies or CRM records, and then gives it to a language model to write the answer. It makes responses more accurate, current and traceable.
Retrieval-Augmented Generation (RAG) explained
Instead of relying on what a model memorized during training, a RAG system searches your content at the moment of the question, usually using vector search over chunks of documents, and passes the best matches into the prompt. The model answers from that material and can cite where each point came from.
Most AI service agents, including those in Agentforce and HubSpot Breeze, use some form of RAG over a knowledge base. Answer quality depends heavily on how current, well-structured and well-tagged that content is.
How Vantage Point helps: we prepare knowledge content and data for RAG and test answer quality before agents go live.
Frequently asked questions
Why use RAG instead of training a custom model?
RAG is cheaper, faster to update and easier to audit. When your policy changes you update the document, not the model.
What makes RAG answers go wrong?
Out-of-date or conflicting source content, poor document structure, and retrieval that pulls the wrong passages.