动态工作流让模型更擅长处理专业领域任务
To be clear, I don't necessarily agree with the post I am quoting. I see a growing trend of people s...
朋友，动态工作流这个概念很实用，能帮你解决模型在专业领域表现不佳的问题，比单纯依赖模型本身更有效。
动态工作流表明，模型在通用任务上表现良好，但在高度专业化的领域（如特定行业知识）会失败。这解释了为什么专业领域仍然需要强大的工作流。最佳工作流应包含强上下文、工具、记忆、验证器和评估，给模型留出推理空间。
To be clear, I don't necessarily agree with the post I am quoting. I see a growing trend of people s...
To be clear, I don't necessarily agree with the post I am quoting. I see a growing trend of people saying that the models will figure out harnesses on their own. To some extent, yes. Just look into dynamic workflows, and you will see it. If you use them extensively, you will also notice how terrible models are at this. And it makes sense because models are generalized and fail miserably on highly specialized contexts or where a lot of prior knowledge and data is used. My point is that harnesses still matter enormously. We need more fluid harnesses that self-improve or continually improve as new models and capabilities land. This is indeed part of your intelligence stack as it stands. The best harnesses are minimal and adaptive: strong context, tools, memory, verifiers, and evals, with room for the model to reason. 💬 0 🔄 0 ❤️ 0 👀 140 ⚡