VAST AI OS will leverage NVIDIA libraries to accelerate both compute and data services for RAG, vector search, real-time SQL, ...

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

Databricks, Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Fabric – to see how they address rapidly evolving ...

AI’s power is premised on cortical building blocks. Retrieval-Augmented Generation (RAG) is one of such building blocks enabling AI to produce trustworthy intelligence under a given condition. RAG can ...

These new models are specially trained to recognize when an LLM is potentially going off the rails. If they don’t like how an interaction is going, they have the power to stop it. Of course, every ...

AT&T's chief data officer shares how rearchitecting around small language models and multi-agent stacks cut AI costs by 90% at 8 billion tokens a day.

SQL will continue to serve as the lingua franca but the world of data will speak in graphs, vectors, LLMs too– and relational databases will stay but not in the same chair. Here’s why?

AI’s next leap won’t come from bigger models or longer context windows, but from better-organized knowledge.

Accessing PHI for development and testing is often blocked by stringent HIPAA compliance requirements. Learn how synthetic data helps engineers build tools to close care gaps and improve HEDIS scores.

Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower cost than frontier models.