Unverified50% confidenceSolutionExact time
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
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Long-term
Relevance
7/21/2026
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Unverified在RAG流程返回大量候选文档片段时，可先按相关性排序再截取前3-5项传入大模型以控制输入长度72% similarUnverifiedTraditional RAG (Retrieval-Augmented Generation) approaches suffer from indexing lag in large-scale active codebases, potentially retrieving deleted or outdated functions.70% similarUnverified召回加重排（Recall + Rerank）先用 embedding 或关键词检索召回 Top 10 候选，再交给大模型做最终裁决，与RAG思路一致66% similarUnverified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66% similarVerified检索增强生成（RAG）是主流幻觉抑制技术，在模型生成回答前从外部知识库检索相关文档片段作为上下文注入提示词65% similar
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