Databricks' Instructed Retriever Beats Traditional RAG Data Retrieval by 70% — Enterprise Metadata Was the Missing Link

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…

RAG '2.0': the Instructed Retriever links AI agents to the right data

The Instructed Retriever is set to replace RAG on a large scale. Databricks sees a 70 percent improvement with the architecture.

Databricks says its Instruction Retrieval offers better AI answers than RAG in the enterprise

Databricks is joining the AI software vendors quietly admitting that old-fashioned deterministic methods can perform much better than generative AI’s probabilistic approach in many applications. Its new “Instructed Retriever” architecture combines old-fashioned database queries with the similarity search of RAG (retrieval-augmented generation) to offer more relevant responses to users’ prompts. Everything about retrieval-augmented generation (RA…