Ask HN: 如何为 AI 应用实现最佳的日志记录和审计追踪方案？
3 分•作者: devstatic•6 个月前
最近我一直在尝试一个小型的人工智能项目，并开始思考围绕提示、响应、模型调用等进行日志记录的问题。<p>对于传统系统，可观测性工具可以处理大部分这类问题，但对于基于大型语言模型（LLM）的应用程序，标准方法似乎不太明确，特别是当你需要适当的审计跟踪来进行调试或合规性检查时。<p>很好奇大家在生产环境中是如何处理这个问题的。<p>大家是主要构建自己的日志记录管道，还是有可靠的工具/平台可以帮助存储和审计 LLM 交互？
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so i’ve been experimenting with a small AI-based project recently and started thinking about logging around prompts, responses, and model calls etc etc.<p>for traditional systems observability tools handle most of this, but with LLM-based apps it feels less clear what the standard approach is, especially if you need proper audit trails for debugging or compliance.<p>curious how teams are handling this in production<p>are people mostly building their own logging pipelines, or are there reliable tools/platforms that help with storing and auditing LLM interactions?