📄 中文摘要
在询问 AI 助手如何进行潜在客户资格评估时，RAG 模型会根据余弦相似度从向量存储中检索出前五个相关片段，并将其提供给模型。这可能导致模型生成的答案包含多个不相关的段落，虽然有时能拼凑出合理的回答，但也可能出现自信却错误的答案。RAG 模式在处理非结构化文档、广泛搜索和快速检索方面表现出色，但在知识具有明确层次和多级关系时，仅依靠相似性检索可能会丧失答案的结构性和意义。
RAG 找到片段，TrailGraph 找到答案。这两者有什么区别？
出处: RAG finds chunks. TrailGraph finds answers. Here's the difference.
发布: 2026年3月26日
📄 English Summary
RAG finds chunks. TrailGraph finds answers. Here's the difference.
When asking an AI assistant how lead qualification works, the RAG model retrieves the top five chunks from a vector store based on cosine similarity and presents them to the model. This can result in the model generating answers that include multiple unrelated paragraphs, which may sometimes cohere but can also lead to confidently incorrect responses. While RAG is effective for unstructured documents, broad searches, and fast retrieval, it can lose the structural integrity and meaningfulness of answers when knowledge has clear hierarchies and multi-level relationships, as retrieval by similarity alone may not suffice.