Unverified50% confidenceSolutionExact time
对于本地部署的小模型，RAG不改动模型权重，文档更新只需重建索引，答案可溯源
1
Sources
50%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
UnverifiedIn RAG systems, relying solely on replacing a stronger vector model to improve recall rate is insufficient, and is regarded as a shallow understanding that amounts to nothing more than knowing how to call an API.69% similarUnverifiedFine-tuning burns knowledge into model weights, making knowledge updates costly as they require retraining; RAG stores knowledge in an external database, so updates only require modifying the database without changing the model.66% similarUnverified检索增强少样本（RAG-augmented Few-shot）方法从历史处理记录中动态检索最相关案例作为示例，使模型持续适应新类型异常而无需重新训练66% similarUnverifiedRAG选型决策规则：数据简单更新少只需相似检索选向量RAG，强调实体关系多跳推理选Graph RAG，需要可导航可溯源自动更新选编译式RAG65% similarUnverified传统RAG系统依赖预建索引，而实时信息流要求索引几乎零延迟更新65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/533259API
curl https://kongchang.com/api/v1/knowledge/claims/533259MCP
get_claim(id=533259)