LinkedIn's feed reaches more than 1.3 billion members — and the architecture behind it hadn't kept pace. The system had accumulated five separate retrieval pipelines, each with its own infrastructure and optimization logic, serving different slices of what users might want to see. Engineers at the company spent the last year tearing that apart and replacing it with a single LLM-based system. The result, LinkedIn says, is a feed that understands professional context more precisely and costs less to run at scale.The redesign touched three layers of the stack: how content is retrieved, how it's ranked, and how the underlying compute is managed. Tim Jurka, vice president of engineering at LinkedIn, told VentureBeat the team ran hundreds of tests over the past year before reach [...]
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 [...]
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 [...]
LinkedIn is launching its new AI-powered people search this week, after what seems like a very long wait for what should have been a natural offering for generative AI.It comes a full three years afte [...]
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 [...]
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 [...]
Redis built its name as the caching layer that kept web applications from collapsing under load. The problem it is targeting now has the same structure but is harder to solve: production AI agents fai [...]
Most enterprise RAG pipelines start the same way: a text parser converts web pages and documents into plain text so they can be chunked and indexed for retrieval. That conversion step destroys retriev [...]