提示缓存正成为管理大型语言模型 (LLM) 相关成本和延迟的关键策略。通过存储和重用先前的响应，应用程序可以显著减少 API 调用，从而为缓存的查询带来高达 90% 的潜在节省和亚毫秒级的响应时间。存在两种主要方法：精确匹配缓存，需要完全相同的请求；以及语义缓存，用于识别相似的含义。这两种方法都可以分层使用以优化性能和成本效益，尽管它们并非适用于所有用例，例如高度创意生成或个性化响应。 AI
影响 提示缓存策略正变得对于使 LLM 应用在经济上可行和大规模高性能至关重要。
排序理由 该集群讨论了 LLM 应用中提示缓存的工具和技术，重点关注成本和延迟优化。
AI 生成摘要 · Google Gemini · 来自 4 个来源。 我们如何撰写摘要 →
<p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Ff40vz36odc5ghcl622pw.png"><img alt="Best Prompt Cach…
<p>The cheapest LLM request is the one you don't send. If the same question shows up twice, there's no reason to pay twice — the model's answer hasn't changed, and the user doesn't care where it came from.</p> <p>That's all prompt caching is. You store the response the first time…
<h2> Introduction </h2> <p><a href="https://pg-blogs.netlify.app/posts/10-building-reliable-llm-apps-in-python/" rel="noopener noreferrer">https://pg-blogs.netlify.app/posts/10-building-reliable-llm-apps-in-python/</a> closed with a section on picking the right model per task and…
<h2> Introduction </h2> <p>We already covered picking the right model tier for the task and caching a large shared prefix in <a href="https://pg-blogs.netlify.app/posts/11-building-reliable-llm-apps-in-java/" rel="noopener noreferrer">https://pg-blogs.netlify.app/posts/11-buildin…