Why enterprise RAG systems fail: Google study introduces ‘sufficient context’ solution

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 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). This approach makes it possible to determine if an LLM has enough information to answer a query accurately, a critical factor for developers building real-world enterprise applications where reliability and factual correctness are paramount. The persistent challenges of RAG RAG systems have become a cornerstone for building more factual and verifiable AI applications. However, these systems can exhibit undesirable traits. They might confidently provide incorrect answers even when presented with retrieved evidence, get distracted by irrelevant information in the context, or fail to extract answers from long text snippets properly. The researchers state in their ... Read full article.