Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.

A new study from Google researchers introduces "sufficient context," a novel perspective for understanding and improving retrieval augmented generation (RAG) systems in large language models (LLMs).

Retrieval-Augmented Generation (RAG) is rapidly emerging as a robust framework for organizations seeking to harness the full power of generative AI with their business data. As enterprises seek to ...

We’ve been living through the generative AI boom for nearly a year and a half now, following the late 2022 release of OpenAI’s ChatGPT. But despite transformative effects on companies’ share prices, ...

The rapid advancements in artificial intelligence (AI) have led to the development of powerful large language models (LLMs) that can generate human-like text and code with remarkable accuracy. However ...

Retrieval augmented generation, or 'RAG' for short, creates a more customized and accurate generative AI model that can greatly reduce anomalies such as hallucinations. As more organizations turn to ...