Unverified50% confidenceTradeoffExact time
Pure generative models mix all training data before producing output, making it impossible for users to trace the evidence chain behind any single conclusion—a limitation compared to the RAG retrieval mechanism.
1
Sources
50%
Confidence
Long-term
Relevance
8/6/2026
First Seen
Sources
Perplexity深度使用指南：高阶技巧与实战场景解析
Related Claims
Unverified微调通过在私有数据上继续训练来更新模型权重，RAG则在不修改模型的前提下通过检索外挂知识库增强回答75% similarUnverifiedIf the synthetic data generation process omits correlations between key variables, models trained on such data often perform poorly in real-world deployment environments.74% similarUnverified蒸馏训练范式导致开源模型学习到经过高度过滤、几乎不包含失败和纠错过程的数据分布，使模型形成不愿自我纠正的性格73% similarUnverified模型本身对准确性毫无概念，它只是模仿训练数据中观察到的统计分布69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/694353API
curl https://kongchang.com/api/v1/knowledge/claims/694353MCP
get_claim(id=694353)