Data teams building AI agents keep running into the same failure mode. Questions that require joining structured data with unstructured content, sales figures alongside customer reviews or citation counts alongside academic papers, break single-turn RAG systems. New research from Databricks puts a number on that failure gap. The company's AI research team tested a multi-step agentic approach against state-of-the-art single-turn RAG baselines across nine enterprise knowledge tasks, reporting gains of 20% or more on Stanford's STaRK benchmark suite and consistent improvement across Databricks' own KARLBench evaluation framework. The results make the case that the performance gap between single-turn RAG and multi-step agents on hybrid data tasks is an architectural problem, no [...]
There is a lot of enterprise data trapped in PDF documents. To be sure, gen AI tools have been able to ingest and analyze PDFs, but accuracy, time and cost have been less than ideal. New technology fr [...]
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 a [...]
Five years ago, Databricks coined the term 'data lakehouse' to describe a new type of data architecture that combines a data lake with a data warehouse. That term and data architecture are n [...]
There is no shortage of AI benchmarks in the market today, with popular options like Humanity's Last Exam (HLE), ARC-AGI-2 and GDPval, among numerous others.AI agents excel at solving abstract ma [...]
Many enterprises running PostgreSQL databases for their applications face the same expensive reality. When they need to analyze that operational data or feed it to AI models, they build ETL (Extract, [...]
Content creation is one of the biggest struggles for many marketers and business owners. It often requires both time and financial resources, especially if you plan to hire a writer.Today, we have a f [...]