Dash如何利用上下文工程实现更智能的AI

When we first built Dash, it looked like most enterprise search systems: a traditional RAG pipeline that combined semantic and keyword search across indexed documents. It worked well for retrieving information and generating concise answers. But as teams began using Dash for more than just finding content—for example, asking it to interpret, summarize, and even act on what it found—we realized that retrieval alone wasn’t enough. The natural progression from “what is the status of the identity project” to “open the editor and write an executive summary of the projects that I own” required Dash to evolve from a search system into an agentic AI.

当我们第一次构建Dash时，它看起来像大多数企业搜索系统：一个传统的RAG管道，结合了语义和关键词搜索，跨索引文档。它在检索信息和生成简洁答案方面表现良好。但是，当团队开始使用Dash进行不仅仅是查找内容时——例如，要求它解释、总结，甚至对找到的内容采取行动——我们意识到仅仅检索是不够的。从“身份项目的状态是什么”到“打开编辑器并写下我拥有的项目的执行摘要”的自然进展，要求Dash从一个搜索系统演变为一个代理AI。

That shift introduced a new kind of engineering challenge: deciding what information and tools the model actually needs to see to reason and act effectively. This has been popularized as context engineering, the process of structuring, filtering, and delivering just the right context at the right time so the model can plan intelligently without getting overwhelmed. We started thinking about how these ideas applied inside Dash itself, including how the model planned, reasoned, and took action on a request. Instead of simply searching and summarizing results, it now plans what to do and carries out those steps.

这种转变引入了一种新的工程挑战：决定模型实际上需要看到哪些信息和工具，以便有效地推理和行动。这被称为 上下文工程，是指在正确的时间结构化、过滤和传递恰当的上下文，以便模型能够智能地规划而不被淹没。我们开始思考这些想法如何应用于 Dash 本身，包括模型如何规划、推理和对请求采取行动。它现在不仅仅是搜索和总结结果，而是规划该做什么并执行这些步骤。

At the same time, adding tools into Dash’s workflow created new tradeoffs around how context is managed. Precision in what you feed the model is critical in any RAG system, and the same lesson applies to agentic systems. Supplying the model with only the most relevant context, and not just more of it, consistently leads to better results. Below, we’ll walk through how we’ve been building bett...