Retrieval-Augmented Generation

In B2B sales, Retrieval-Augmented Generation allows AI agents to access and incorporate relevant information from a large corpus during the conversation. This ensures that responses are more accurate and contextually appropriate, leading to better customer interactions and outcomes.

AI agents increasingly use this technique to improve their performance in generating persuasive and informative content for customers. However, they may need human approval on certain actions or decisions to ensure ethical and compliant interactions.

A typical RAG pipeline chunks documents, embeds them into a vector store, retrieves the most relevant chunks at query time, and passes them to the model as context, which cuts hallucination and lets answers cite a source. In go-to-market, RAG lets an agent ground a reply in your actual pricing, docs, or account history rather than its training data. Grounding improves accuracy but does not remove the need for a human gate on anything the agent then writes or sends.