Retrieval Augmented Generation (RAG) systems are transforming the landscape of artificial intelligence by enhancing pre-trained language models (LLMs) with external knowledge. Organizations are utilizing vector databases to create RAG systems tailored to internal data sources, boosting the capabilities of LLMs and revolutionizing how AI interprets user queries.
RAG systems augment the pre-trained knowledge of LLMs with enterprise or external knowledge to generate context-aware, domain-specific responses. Many organizations are leveraging vector databases to build RAG systems that incorporate enterprise internal data sources, deriving higher business value from large language foundation models.
RAG systems, as defined by Prasad Venkatachar, Senior Director of Products and Solutions at Pliops, extend the capabilities of LLMs by dynamically integrating enterprise data sources with information during the inference phase. The process involves a Retriever retrieving relevant context from data sources, the Augment process integrating the retrieved data with user queries, and the generation process producing relevant responses to user queries based on the integrated context.
RAG is an increasingly significant area in natural language processing (NLP) and GenAI, offering enriched responses to customer queries with domain-specific information in chatbots and conversational systems. Various platforms such as AlloyDB from Google, CosmosDB from Microsoft, Amazon DocumentDB, MongoDB in Atlas, Weaviate, Qdrant, and Pinecone provide vector database functionality to serve as a foundation for organizations to develop RAG systems.
How RAG Can Benefit
The advantages of RAG can be categorized into the following areas:
- Bridging Knowledge Gaps: RAG helps bridge knowledge gaps by equipping the model with additional domain-specific information, enabling it to handle and respond to queries effectively.
- Reduced Hallucination: RAG systems access and interpret relevant information from external sources, ensuring answers are based on real-world data and facts, crucial for tasks requiring accuracy and up-to-date knowledge.
