A simple definition
Retrieval-augmented generation, often called RAG, is a method that lets an AI system retrieve relevant information before generating an answer. Instead of relying only on the model’s memory, the system uses selected company sources.
Why it matters
RAG is important because most valuable enterprise questions depend on private, current, and permissioned information. A general model cannot know your latest contracts, policies, customer issues, or operating metrics.
What can go wrong
Poor source quality, weak permissions, stale documents, and vague prompts can all reduce answer quality. RAG is not magic. It requires disciplined knowledge management and evaluation.
The CEO takeaway
RAG is best understood as an intelligence layer over trusted knowledge. The goal is not to experiment with chatbots. The goal is to make the organization’s knowledge easier to apply.
InfoPlus AI is designed around one idea: better enterprise decisions start with better access to trusted information.