O que é RAG (Retrieval-Augmented Generation)?
Architecture that allows an AI to access a private knowledge base before generating a response, preventing hallucinations and ensuring accuracy.
RAG (Retrieval-Augmented Generation) is the industry-standard technique for connecting a company's private database (documents, CRM, spreadsheets) to a Large Language Model (LLM).
The Hallucination Problem
If you ask a generic corporate ChatGPT "What is the price of our Pro plan?", it doesn't know the answer. It might "hallucinate" (invent) a number based on language patterns. For the B2B market, this is unacceptable.
How RAG Solves It
RAG works in three fundamental steps:
- Retrieval: When a question is asked, the system does not ask the AI to invent the answer. It first performs a semantic search in a vector database containing all the company's policies and manuals.
- Augmentation: The system combines the user's original question with the recovered official text.
- Generation: The AI receives the strict instruction: "Answer the question based ONLY on these documents".
The result is a fluid, intelligent response that is 100% anchored in your company's truth (the Ground Truth). At Infinity Solutions, RAG is the backbone of all our autonomous assistants and agents.