📄 中文摘要
构建在大型语言模型（LLM）之上的应用主要面临的是周边问题，包括上下文管理、有效提示的设计以及处理需要结构化的数据的非结构化文本。文中介绍了三种工具，旨在解决这些常见问题。其中，Token Counter 用于处理上下文溢出的问题，尤其是在处理文档、维护对话历史或链式调用多个 LLM 时，能够有效避免错误并提升工作效率。这些工具的使用可以显著改善 LLM 的工作流程，帮助开发者更好地管理和利用模型的能力。
三种 API 调用显著提升你的 LLM 工作流程
出处: Three API Calls That Make Your LLM Workflow Dramatically Better
发布: 2026年3月2日
📄 English Summary
Three API Calls That Make Your LLM Workflow Dramatically Better
Building applications on top of large language models (LLMs) primarily involves addressing surrounding issues such as context management, crafting effective prompts, and handling unstructured text that needs to be converted into structured data. The article introduces three tools designed to tackle these common problems. Among them, the Token Counter is particularly useful for managing context overflows, especially when processing documents, maintaining conversation history, or chaining multiple LLM calls. Utilizing these tools can significantly enhance the LLM workflow, enabling developers to better manage and leverage the capabilities of the model.