已过期50% 置信事实时间未知
RAG addresses LLM hallucination issues and knowledge timeliness problems by retrieving relevant document fragments and injecting them as context into the LLM prompt
1
来源数
50%
置信度
中期 (~90 天)
时效性
2026/7/2
首次发现
此条事实已过期，仅作历史留存，不再代表当前状态
有效期至：2026/9/30(已过期)
来源
相关事实
待验证RAG architecture retrieves relevant document fragments from an external knowledge base before the LLM generates a response, injecting retrieved content as context into the prompt.79% 相似部分验证RAG (Retrieval-Augmented Generation) was the dominant paradigm for LLM applications in 2023, solving issues of outdated model knowledge and hallucinations by injecting retrieval results from external knowledge bases into the model's context.78% 相似
引用此条事实
Stable URI
https://kongchang.com/claim/60481API
curl https://kongchang.com/api/v1/knowledge/claims/60481MCP
get_claim(id=60481)