Verified65% confidenceFactExact time
The "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.
3
Sources
65%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
Kagi's AI Toggle: A Product Philosophy That Puts Users Back in Control
hackernewshackernews7/3/2026
Related Claims
UnverifiedMost 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.73% similarUnverifiedThe original post proposed that the three hurdles for RAG in production environments are: answer filtering based on user permissions, traceability of the answer generation process, and output contradictions caused by inconsistency across multiple data sources.66% similarVerifiedThe core limitations of traditional RAG solutions include: document chunking destroying the holistic semantic structure of knowledge, vector similarity retrieval failing to capture deep logical associations, and each query being independent with no ability to accumulate structured knowledge.65% similarUnverified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64% similarUnverifiedIn multi-turn conversations, elliptical queries with anaphora perform poorly when used directly for vector retrieval, so query rewriting using an LLM to rewrite them into self-contained queries is a common approach64% similar
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