生产环境中的大多数RAG应用自信地犯错

We will uncover reasons for retrieval-augmented generation failure at scale. The system architectural improvements, plus an ML-grounded solution for retrieval sufficiency, context ambiguity, generation entropy, and the semantic illusion to prevent plaguing modern AI pipelines. The promise of Retrieval-Augmented Generation (RAG) was elegantly simple: the AI framework will integrate large language models with proprietary data, reduce hallucinations, and ship enterprise AI faster by optimizing performance and giving high-quality results by connecting with external knowledge bases. Four years ago, RAG revolutionized GenAI and NLP models mainly to keep models up-to-the- minute, relevant, cost-effective, and adaptable. Today, RAG powers compliance copilots, customer support agents, and internal knowledge assistants across Fortune 500 stacks. …