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.

Ten AI concepts to know in 2026, including LLM tokens, context windows, agents, RAG, and MCP, for building reliable AI apps.

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

Why Most Retrieval-Augmented Generation Systems Fail Online Retailers — And the Practical Fixes That Turn Customer Interactions Around ...

The open-source AI model landscape in 2026 is genuinely impressive — and genuinely confusing. Eighteen months ago, the gap between the best open models and ...

As the demand for AI grows, Telenor is tapping its decades of experience in operating critical infrastructure to help meet the stringent national and EU requirements around data controls and digital ...

AI auditors resemble financial auditors, except they observe and report on the behavior of AI transactions rather than ...

When your AI assistant calculates revenue, bonuses, VAT or financial summaries, it isn’t doing math. It’s telling a convincing story about numbers.

Databricks and Tonic.ai have partnered to simplify the process of connecting enterprise unstructured data to AI systems to reap the benefits of RAG. Learn how in this step-by-step technical how-to.

Your notes, searchable by meaning. An MCP server that gives Claude Code semantic search over your Obsidian vault. Ask questions in natural language, get answers from your own documents. You have 500 ...

Abstract: The current massive development of RAG-based chatbots demonstrates the critical importance of personalization and response quality in language models. The personalization process in RAG lies ...

If you spend a lot of time around Harley-Davidson bikes, you'll notice a peculiarity among these American motorcycles. Each model has a code consisting of letters and numbers. However, these codes are ...