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 [...]
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 [...]
When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing [...]
Agents are the trendiest topic in AI today — and with good reason. Taking gen AI out of the protected sandbox of the chat interface and allowing it to act directly on the world represents a leap for [...]