Why Your Biomedical RAG Is Hiding Contradictions From You
Standard biomedical RAG resolves conflicting evidence silently in roughly three of every four queries. The fix is structural, not informational — and most of the upstream complexity teams add does not help. Ask a retrieval-augmented clinical assistant a simple question — does melatonin help with jet lag? — and the literature it retrieves will not agree with itself. The Cochrane review concluded that melatonin is remarkably effective, with eight of ten included trials showing reduced jet lag on… [...]
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Databricks research shows multi-step agents consistently outperform single-turn RAG when answers span databases and documents
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 co [...]
The retrieval rebuild: Why hybrid retrieval intent tripled as enterprise RAG programs hit the scale wall
Something shifted in enterprise RAG in Q1 2026. VB Pulse data spanning January through March tells a consistent story: the market stopped adding retrieval layers and started fixing the ones it already [...]
Why Google’s File Search could displace DIY RAG stacks in the enterprise
By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups could be an [...]
With 91% accuracy, open source Hindsight agentic memory provides 20/20 vision for AI agents stuck on failing RAG
It has become increasingly clear in 2025 that retrieval augmented generation (RAG) isn't enough to meet the growing data requirements for agentic AI.RAG emerged in the last couple of years to bec [...]
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 a [...]
Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into [...]
The RAG era is ending for agentic AI — a new compilation-stage knowledge layer is what comes next
The vector database category is undergoing a shift in response to the needs of agentic AI. The retrieval-augmented generation (RAG)-to-vector database pipeline doesn't cut it anymore; agentic AI [...]
Stop graphing everything: When GraphRAG actually beats vector RAG
If you have built anything with retrieval-augmented generation (RAG) in the last two years, you have lived its central frustration: You chop your documents into chunks, embed them, retrieve the top fe [...]