venturebeat
How LinkedIn replaced five feed retrieval systems with one LLM model, at 1.3 billion-user scale

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 [...]

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venturebeat
Enterprises are measuring the wrong part of RAG

Enterprises have moved quickly to adopt RAG to ground LLMs in proprietary data. In practice, however, many organizations are discovering that retrieval is no longer a feature bolted onto model inferen [...]

Match Score: 276.89

venturebeat
Inside LinkedIn’s generative AI cookbook: How it scaled people search to 1.3 billion users

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 [...]

Match Score: 142.53

venturebeat
This tree search framework hits 98.7% on documents where vector search fails

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 [...]

Match Score: 136.96

venturebeat
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 [...]

Match Score: 130.81

venturebeat
Agents don't replace vector search - they make it harder to get right

What's the role of vector databases in the agentic AI world? That's a question that organizations have been coming to terms with in recent months.<br /> <br /> The narrative had [...]

Match Score: 120.27

venturebeat
How xMemory cuts token costs and context bloat in AI agents

Standard RAG pipelines break when enterprises try to use them for long-term, multi-session LLM agent deployments. This is a critical limitation as demand for persistent AI assistants grows.xMemory, a [...]

Match Score: 109.98

venturebeat
From shiny object to sober reality: The vector database story, two years later

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 [...]

Match Score: 102.16

Destination
The best smart scales for 2025

The New Year is here and there’s no better time to kickstart those health and fitness goals. Whether you’re looking to shed a few holiday pounds, track your muscle gains or simply stay on top of a [...]

Match Score: 99.66

venturebeat
Under the hood of AI agents: A technical guide to the next frontier of gen AI

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 [...]

Match Score: 89.93