Unverified50% confidenceOpinionExact time
Most RAG (Retrieval-Augmented Generation) tools can provide answers, but struggle to let users quickly confirm which specific sentence in the document the answer came from.
1
Sources
50%
Confidence
Long-term
Relevance
8/31/2026
First Seen
Sources
Related Entities
Related Claims
Unverified大多数RAG（检索增强生成）实现只解决了'检索到什么'的问题，却忽视了检索到的内容是否可信、是否互相矛盾的更深层问题78% similarUnverifiedThe "answer engine" paradigm represented by Perplexity abandons returning lists of web pages and instead directly generates natural language answers with cited sources, which is essentially the productization of RAG technology.73% similarUnverifiedChunks that are too large dilute retrieval precision, while chunks that are too small result in incomplete semantics; the chunking strategy directly determines the retrieval quality of a RAG system.73% similarUnverified不同的用户提问表述会生成不同的向量，与文档内容的语义距离也会有所差异，影响RAG检索效果70% similarUnverified传统RAG无法回答'知识库里有哪些文档'这类元数据问题，因为它本质上只能检索内容片段69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/828453API
curl https://kongchang.com/api/v1/knowledge/claims/828453MCP
get_claim(id=828453)