Retrieval Augmented Generation: What It Is and Why It Matters for Enterprise AI

DataStax's CTO discusses how Retrieval Augmented Generation (RAG) enhances AI reliability, reduces hallucinations, and transforms information retrieval.

Retrieval Augmented Generation (RAG) has become essential for IT leaders and enterprises looking to implement generative AI. By using a large language model (LLM) and RAG, enterprises can ground an LLM in enterprise data, improving the accuracy of outputs.

But how does RAG work? What are the use cases for RAG? And are there any real alternatives?

TechRepublic sat down with Davor Bonaci, chief technology officer and executive vice president at database and AI company DataStax, to find out how RAG is being leveraged in the market during the rollout of generative AI in 2024 and what he sees as the technology’s next step in 2025.

What is Retrieval Augmented Generation?

RAG is a technique that improves the relevance and accuracy of generative AI LLM model outputs by adding extended or ...