What is RAG in AI?

RAG, or Retrieval-Augmented Generation, is an AI framework that improves the performance of a large language model (LLM) by giving it access to external, up-to-date, or proprietary data. It’s designed to solve the problem of LLMs sometimes providing outdated, inaccurate, or “hallucinated” information because their knowledge is limited to the data they were originally trained on.

How it Works

The RAG process involves two main stages:

1. Retrieval: When a user asks a question, the system first retrieves the most relevant information from a predefined knowledge base (e.g., a company’s internal documents, a private database, or the live internet). This retrieval is often powered by a vector database, which finds semantically similar documents to the user’s query.
2. Augmented Generation: The retrieved information is then added to the user’s original query, creating a new, more detailed prompt. This augmented prompt is fed to the LLM. The LLM then generates a response using this new context, which makes the answer more accurate, relevant, and grounded in facts.

Key Benefits

• Factuality: It significantly reduces the risk of “hallucinations” by grounding the model’s responses in a verifiable, external knowledge base.
• Up-to-Date Information: It allows LLMs to access real-time information without the need for expensive and time-consuming retraining.
• Customization: It enables companies to use public models on their own private or domain-specific data, such as internal policies or customer support logs.
• Cost-Effective: It provides a more affordable alternative to continuously retraining or fine-tuning a massive model with new information.