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 has. Call it the retrieval rebuild.The survey covered three consecutive monthly waves from organizations with 100 or more employees, with between 45 and 58 qualified respondents per month across platform adoption, buyer intent, architecture outlook and evaluation criteria. The data should be treated as directional.Enterprise intent to adopt hybrid retrieval tripled from 10.3% to 33.3% in a single quarter — even as 22% of qualified enterprise respondents reported having no production RAG systems at all. For data engineers and enterprise architects building agentic AI infrastructure, the data [...]
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 [...]
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 [...]
Enterprise teams that fine-tune their RAG embedding models for better precision may be unintentionally degrading the retrieval quality those pipelines depend on, according to new research from Redis.T [...]
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 [...]
A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents.The classic RAG workflow (chunk documents, calculate e [...]
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 [...]