Databricks' Instructed Retriever beats traditional RAG data retrieval by 70% — enterprise metadata was the missing link

A core element of any data retrieval operation is the use of a component known as a retriever. Its job is to retrieve the relevant content for a given query. In the AI era, retrievers have been used as part of RAG pipelines. The approach is straightforward: retrieve relevant documents, feed them to an LLM, and let the model generate an answer based on that context.While retrieval might have seemed like a solved problem, it actually wasn't solved for modern agentic AI workflows.In research published this week, Databricks introduced Instructed Retriever, a new architecture that the company claims delivers up to 70% improvement over traditional RAG on complex, instruction-heavy enterprise question-answering tasks. The difference comes down to how the system understands and uses metadata."A lo