Prompt Engineering、提示词模式与上下文工程

OpenAI 发布了 GPT-5.5 的提示工程指南，强调新模型需重新调优而非直接复用旧提示。

2026 will be the year of AI Harness. Using engineering methods like guardrails, validation, and automation processors, unreliable AI Agents can be transformed into stable, controllable systems without modifying Prompts, marking key infrastructure for AGI.

In agentic AI systems, prompt engineering has evolved into context engineering: reliable behavior requires deliberate design of four components—system prompt, tools, examples, and context state management—otherwise, context rot causes behavioral drift across multi-step reasoning, as identified by Anthropic's research.

Alex Lupsasca of OpenAI demonstrates that GPT-5 series models have achieved breakthrough scientific reasoning, reproducing his months-long theoretical physics paper in just 11 minutes — signaling AI’s transformation of fundamental scientific discovery.

Anthropic 在 Claude Opus 4.7 系统提示中新增儿童安全标签、工具搜索机制，并优化交互逻辑以减少冗长和侵扰性行为。

AI体验设计正从提示工程转向约束优先架构，以解决大模型幻觉与可信度问题。

Anthropic推出Claude Mods功能，支持对话上下文管理、子代理执行、结构化输出和UI自定义，通过插件系统实现扩展。

AWS官方博客详解Quick组件提示工程实践，揭示精准目标定义、数据源筛选等关键技巧，避免常见陷阱。

明确的提示结构能显著提升Amazon Quick的AI功能输出质量，本文系统阐述了提示工程的核心原则与框架。

Jev是TypeSafe AI的新型决策模型，通过类型安全的结构化输出实现低成本高效分类，已成功应用于PR分类、Gmail处理等场景，单次调用成本仅4美分。

Jev通过结构化输出实现更低延迟和成本，但无法替代LLM judge的解释能力，在技术问答评估中表现优异。

TypeSafe.AI的System One模型Jev通过结构化输出和RLCD训练法，在决策类任务中优于传统LLM，尤其适合意图分类场景。