随着人工智能和大模型技术的快速发展，越来越多的开发者开始探索如何将 AI 技术真正应用到实际开发流程中。然而，在实际使用过程中，许多人仍然会遇到各种问题，例如 Prompt 设计不合理导致输出质量不稳定、上下文控制不当造成 Token 消耗过高，以及在真实项目中如何高效调用模型等。这些问题不仅影响开发效率，也会大幅增加 AI 应用的运行成本。 为帮助开发者更好地理解和使用大模型工具，**匠人学院（JR Academy）**特别分享了 《OpenClaw 模型经验分享：Kimi 模型实操 + 避坑 + 省 Token》 的技术实战视频。本次分享以真实开发经验为基础，从实践角度出发，系统讲解了如何在 OpenClaw 环境中高效使用 Kimi 模型，并总结了一系列在实际项目中非常实用的技巧和方法。 视频内容涵盖多个核心部分。首先从整体层面介绍 Kimi 模型的基本能力与应用场景，帮助开发者理解在什么情况下适合使用该模型。随后进入 实际操作演示，讲解如何通过合理设计 Prompt 来提升输出质量，并通过控制上下文结构来减少不必要的 Token 消耗。同时，视频还重点分享了开发者在使用过程中常见的“坑”，例如 Prompt 过长导致成本增加、上下文信息冗余、模型输出偏离任务目标等，并提供对应的优化策略与解决方案。 此外，本次分享还特别讨论了 AI 应用开发中的成本控制问题。通过合理拆分任务、优化 Prompt 结构以及精简上下文内容，可以显著降低 Token 消耗，在保证结果质量的同时提高整体开发效率。 本视频适合 AI 应用开发者、Prompt 工程师、AI 产品经理以及对大模型开发实践感兴趣的技术从业者。如果你正在探索如何在真实项目中高效使用大模型，或者希望减少 AI API 调用成本并提升开发效率，这场经验分享将为你提供非常有价值的实践参考。 With the rapid advancement of artificial intelligence and large language models, more developers are exploring how to integrate AI capabilities into real-world development workflows. However, in practical usage, many developers encounter challenges such as poorly designed prompts leading to unstable outputs, inefficient context management causing excessive token consumption, and uncertainty about how to effectively integrate models into production environments. These issues can significantly impact development efficiency and increase the operational cost of AI-powered applications. To help developers better understand and utilize large language models in practice, JR Academy presents a technical sharing session titled “OpenClaw Model Experience Sharing: Kimi Model Practical Guide, Pitfalls, and Token Optimization.” This session is based on real-world development experience and focuses on practical strategies for effectively using the Kimi model within the OpenClaw environment. The session begins with an overview of the capabilities and common use cases of the Kimi model, helping developers understand when and how it can be best applied. It then moves into hands-on demonstrations, showing how thoughtful prompt design can improve model output quality and how structured context management can significantly reduce unnecessary token usage. The speaker also shares several common pitfalls developers encounter when working with large models, such as overly long prompts, redundant context information, and outputs that drift away from the intended task. Practical solutions and optimization strategies are provided to address these challenges. Another key focus of the session is cost optimization in AI development. By structuring prompts more efficiently, splitting complex tasks into smaller steps, and reducing redundant context, developers can significantly lower token consumption while maintaining high-quality outputs. This video is particularly useful for AI application developers, prompt engineers, AI product managers, and anyone interested in practical large language model development. Whether you are experimenting with AI tools for the first time or looking to optimize your existing workflows, this sharing session provides valuable insights and practical techniques for building more efficient and cost-effective AI applications.