这篇面向营销、写作和开发工作流的长文把“提示词技巧”改写为工作契约:定义 Goal、Context、Output、Boundaries 和 Completion check,再依据任务风险选择 Luna、Terra 或 Sol。它建议把 Luna 用于抽取、分类、转换和高频任务,用更高能力模型承担复杂判断,并强调“reasoning effort 是计算预算,不是真实性开关”。
Goal: Produce a source-backed decision guide for marketing leaders choosing among GPT-5.6 Sol, Terra, and Luna.
Use current OpenAI documentation for product facts. Use practitioner and community reports only as labelled experience, not proof. Compare the models by task risk, latency, cost, review burden, and accepted outcome.
Deliver a publish-ready article with a decision table, role-specific workflows, prompt templates, limitations, and FAQs. Preserve source links next to supported claims. Do not invent benchmarks, access rules, or usage limits.
Before finishing, verify that availability and pricing are current, each workflow has a human approval boundary, and the recommendation lets a reader choose a model.| 工作负载 | 起始模型 | 推理等级 | 原因 |
|---|---|---|---|
| 快速改写、标签、抽取、分类 | Luna | none/low | 快速、便宜、易验证 |
| 常规研究、brief、初稿、仓库探索 | Terra | low/medium | 能力与成本平衡 |
| 重要文章、策略、复杂调试、综合分析 | Sol | medium/high | 更适合交叉约束与证据判断 |
| 高风险调查或架构决策 | Sol | xhigh/max | 仅在结果可验证时提高探索量 |
文章最值得迁移到 Luna 工作流的不是某个角色口令,而是“完成标准先于风格要求”:告诉模型要交付什么、依据什么、不能做什么、结束前检查什么。对于高频任务,应先用 Luna 的 low 或 none 测试接受率,再按失败类型升级模型或推理等级。
本站仅展示 Tabbit 编辑摘要和必要节选;完整内容、上下文与最新版本请查看原始来源。
GPT-5.6 Luna