The modern customer has just one need that matters: Getting the thing they want when they want it. The old standard RAG model embed+retrieve+LLM misunderstands intent, overloads context and misses ...

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.

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