Retrieval Augmented Generation systems are changing the AI ​​landscape again

Retrieval Augmented Generation (RAG) systems are revolutionizing AI by augmenting pre-trained language models (LLMs) with external knowledge. By using vector databases, organizations develop RAG systems that are aligned with internal data sources, expanding LLM capabilities. This merger changes the way AI interprets user questions and delivers contextually relevant answers across domains.

As the name suggests, RAG augments LLMs’ pre-trained knowledge with corporate or external knowledge to generate context-aware domain-specific responses. To get more business value from large language base models, many organizations use vector databases to build RAG systems with internal corporate data sources.

Prasad Venkatachar

Senior Director of Products and Solutions at Pliops.