2 Matching Annotations
  1. Apr 2026
    1. The benefit of using LLMs is that a lot of the initial context gathering can be done in an automated way. An emphasis of focus should be on high signal context – for example, looking through past query history can be high signal in determining the most referenced tables and most common joins, and data modeling solutions like dbt or LookML can provide clear definitions for business metrics.

      这一观点揭示了LLM在上下文构建中的独特价值:自动化高信号上下文的收集。这暗示了未来数据代理的发展可能需要结合LLM的自动化能力与人类的判断力,形成人机协作的上下文构建模式。

    1. There's an old saying that content is king. With agents, context is.

      在 LLM 时代,这是对“上下文窗口”重要性最精辟的注解。Agent 不具备人类的隐性知识和环境感知能力,因此显式的上下文(如 context.json)成为了其行动的基石。这提醒我们,在设计 AI 辅助系统时,构建高质量的上下文生成机制往往比优化模型本身更为关键。