2025年AI大模型应用架构设计十大核心问题深度解析

本文系统剖析大模型应用落地的十大关键技术挑战，涵盖RAG优化、幻觉抑制、有限资源微调、多语言检索系统设计等前沿问题，提供可落地的架构方案与性能优化策略。

一、RAG流水线性能评估体系设计

1.1 多维度评估指标

1.2 核心评估方法

• 检索阶段验证：
  • 通过MRR（平均倒数排名）和rPrec（标准化精度）量化文档排序质量，金融场景要求rPrec≥0.85
  • 使用重排序技术（如Sentence-BERT）提升Top1相关性，实验证明可使准确率提升40%
• 生成阶段验证：
  • 采用FactScore指标分解事实链验证（如生物医学术语的准确表述）
  • 注入对抗性查询检测幻觉，如“猴子喜欢的黄色长条物是什么”应返回“香蕉”而非“猴子糖果”
• 端到端测试：
  • 构建多意图测试集（如“修改地址后查物流又取消赠品”），要求意图识别率＞92%

二、生成式问答系统的幻觉抑制策略

2.1 技术架构优化

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