📄 中文摘要
大多数 RAG 系统出现幻觉并非因为模型本身的弱点，而是由于检索过程的不足。在一个近期的项目中，构建了一个看似完善的 RAG 管道，包括嵌入、向量数据库、Top-K 检索和 LLM 合成。尽管这一系统在表面上运作良好，但在实际应用中却频繁出现幻觉现象。通过分析这些问题，提出了一种混合管道的解决方案，旨在增强检索的有效性，从而减少模型生成错误信息的概率。该方案通过优化检索机制，提升了系统的整体性能和可靠性。
为什么大多数 RAG 系统会出现幻觉 — 我的混合管道如何解决这个问题
出处: Why Most RAG Systems Hallucinate — And How My Hybrid Pipeline Fixes It
发布: 2026年2月28日
📄 English Summary
Why Most RAG Systems Hallucinate — And How My Hybrid Pipeline Fixes It
Most RAG systems hallucinate not due to weaknesses in the model itself, but because of deficiencies in the retrieval process. In a recent project, a seemingly solid RAG pipeline was built, incorporating embeddings, a vector database, top-K retrieval, and LLM synthesis. While this system appeared to function well, it frequently produced hallucinations in practice. By analyzing these issues, a hybrid pipeline solution was proposed to enhance the effectiveness of retrieval, thereby reducing the likelihood of the model generating erroneous information. This approach optimizes the retrieval mechanism, improving the overall performance and reliability of the system.