Five major obstacles are holding back RAG systems in healthcare
Retrieval-augmented generation (RAG) promises to help medical AI systems deliver up-to-date and reliable answers. But a new review shows that, so far, RAG rarely works as intended in real-world healthcare settings—and technical, regulatory, and infrastructure hurdles are slowing its adoption.<br /> The article Five major obstacles are holding back RAG systems in healthcare appeared first on THE DECODER. [...]
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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 [...]
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 [...]
Conversational AI doesn’t understand users — 'Intent First' architecture does
The modern customer has just one need that matters: Getting the thing they want when they want it. The old standard RAG model embed+retrieve+LLM misunderstands intent, overloads context and misses fre [...]
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 [...]
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 [...]
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 [...]
Agent context layers: Enterprises governing their AI data are catching twice as many bad answers as the ones who aren't
Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]
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 [...]