Build Hour: Prompt Caching

AI 总结

• 视频系统介绍了 Prompt Caching 的工作原理，包括前缀匹配、token 阈值与上下文连续性等命中条件。
• 强调在真实应用中通过优化提示词结构可提升缓存命中率，从而减少推理成本并降低响应延迟。
• 给出工程实践建议：使用 Responses API、prompt_cache_key，并持续监测命中率、延迟与 token 节省。
• 包含 Batch Image Processing、Branching Chat、Long Running Compaction 三个演示，展示不同场景下的缓存设计方法。
• 客户案例 Warp 分享了 Prompt Caching 的实际业务效果，补充了落地价值与可行性。

内容摘录

Build faster, cheaper, and with lower latency using prompt caching. This Build Hour breaks down how prompt caching works and how to design your prompts to maximize cache hits. Learn what’s actually being cached, when caching applies, and how small changes in your prompts can have a big impact on cost and performance. 

Erika Kettleson (Solutions Engineer) covers:
• What prompt caching is and why it matters for real-world apps
• How cache hits work (prefixes, token thresholds, and continuity)
• Best practices like using the Responses API and prompt_cache_key
• How to measure cache hit rate, latency, and token savings
• Customer Spotlight: Warp (ttps://www.warp.dev/) led by Suraj Gupta (Team Lead) to explain the impact of prompt caching

👉 Prompt Caching Docs: https://platform.openai.com/docs/guides/prompt-caching
👉 Prompt Caching 101 Cookbook: https://developers.openai.com/cookbook/examples/prompt_caching101
👉 Prompt Caching 201 Cookbook: https://developers.openai.com/cookbook/examples/prompt_caching_201
👉 Follow along with the code repo: http://github.com/openai/build-hours
👉 Sign up for upcoming live Build Hours: https://webinar.openai.com/buildhours

00:00 Introduction
02:37 Foundations, Mechanics, API Walkthrough
12:11 Demo: Batch Image Processing
16:55 Demo: Branching Chat
26:02 Demo: Long Running Compaction
32:39 Cache Discount Pricing Overview
36:03 Customer Spotlight: Warp
49:37 Q&A