Google researchers introduced a method to improve AI search and assistants by enhancing Retrieval-Augmented Generation (RAG) models’ ability to recognize when retrieved information lacks sufficient ...

A new study from Google researchers introduces "sufficient context," a novel perspective for understanding and improving retrieval augmented generation (RAG) systems in large language models (LLMs).

Retrieval-Augmented Generation (RAG) effectively grounds LLM outputs in external knowledge, but does not model the runtime context, such as user identity, session state, or domain constraints, on ...

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 ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Ian Cooper discusses managing asynchronous ...

What if the very systems designed to enhance accuracy were the ones sabotaging it? Retrieval-Augmented Generation (RAG) systems, hailed as a breakthrough in how large language models (LLMs) integrate ...

Use this Neo4j GraphRAG library to build your own knowledge graph-based applications. But why GraphRAG, the concept, in the first place? GraphRAG is an advanced RAG technique which consumes structured ...