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).

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

Retrieval-augmented generation breaks at scale because organizations treat it like an LLM feature rather than a platform discipline. Enterprises that succeed with RAG rely on a layered architecture.

Forbes contributors publish independent expert analyses and insights. I am an MIT Senior Fellow & Lecturer, 5x-founder & VC investing in AI RAG add information that the large language model should ...