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 ...

Retrieval augmented generation (RAG) has quickly risen to become one of the most popular architectures when building AI assistants, especially in scenarios where combining the power of language models ...

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 ...

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach to significantly enhance the capabilities of language models. By seamlessly integrating document retrieval with text ...

Retrieval-augmented generation (RAG) is quickly becoming the foundational architecture for enterprise AI applications that produce information derived from enterprise data sources instead of solely ...

In the digital age, the ability to find relevant information quickly and accurately has become increasingly critical. From simple web searches to complex enterprise-knowledge management systems, ...

Every few months, the enterprise AI conversation resets around the same flawed premise that better models solve the problem. When large language models hallucinate, the instinct is to reach for a ...