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 lot of the systems that were built for retrieval before the age of large language models were really built for humans to ...