English(EN) I Reduced My System Prompt Tokens by 70% Using a Custom Prompt DSL

开发人员使用自定义DSL将LLM提示令牌减少70%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-11 12:10

一位开发人员创建了一种自定义的领域特定语言（DSL）用于系统提示，显著将令牌使用量减少了高达70%。该DSL用更紧凑、机器友好的语法取代了冗长的英文指令。实验表明，这种压缩格式，类似于JSON或代码等结构化数据，被Gemini等模型有效理解，尽管提示长度大幅缩短，但仍保持了高质量的输出。 AI

影响这项技术通过最小化令牌使用量，可以显著降低LLM应用程序的推理成本并提高效率。

排序理由该集群描述了一种优化LLM提示的新颖方法，这是一种工具或技术，而不是核心模型发布或研究论文。

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群描述了一种优化LLM提示的新颖方法，这是一种工具或技术，而不是核心模型发布或研究论文。
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: product, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 88 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

dev.to — LLM tag TIER_1 English(EN) · Kiran Reddy Duvvuru · 2026-06-11 12:10

我使用自定义提示DSL将系统提示令牌减少了70%

What if we've been writing prompts the wrong way? For the last two years, the AI community has focused on writing better prompts. We've all written prompts that look something like this: <blockquote> You are an expert product strategist, UX design…