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).

The modern customer has just one need that matters: Getting the thing they want when they want it. The old standard RAG model embed+retrieve+LLM misunderstands intent, overloads context and misses ...

Retrieval-augmented generation breaks at scale because organizations treat it like an LLM feature rather than a platform discipline. Enterprises that succeed with RAG rely on a layered architecture.

Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG) is a ...