在深度上下文工程的哪个层面，人工智能的输出会变得像人类创作的作品一样？
我一直在思考一个关于在大型语言模型（LLMs）时代的著作权的哲学和伦理问题，我很好奇HN社区是如何划定界限的。
假设你花了几个月深入研究一个小众话题。你做出了自己的发现，整理了自己的见解，并将所有这些精心策划、非常具体的背景信息输入到一个LLM中。你实际上是在构建一个定制的知识库，并根据你自己的思维框架训练模型。
当你最终想写一篇帖子或评论来分享你的发现时，你概述了自己的具体想法，并利用这个经过精心准备的LLM来构建和生成最终的文本。
我想问你几个问题：
1. 在没有“AI生成”免责声明的情况下发布这些内容是否不道德？
2. 实际上展示的知识是谁的？LLM生成的是语法，但语义、见解和深层背景信息都是100%人类来源的。
这与使用代笔、编辑或高度先进的编译器根本上有何不同？如果我在进行背景工程和知识发现的重任，限制自己不利用LLM来构建最终输出似乎有些不合理。然而，互联网仍然普遍将任何AI生成的文本视为本质上“非人类”或低投入的。
人类的见解在哪里结束，AI的生成又从何开始？如果核心思想是你的，那么文本的媒介真的就是信息吗？
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I’ve been wrestling with a philosophical and ethical question regarding authorship in the age of LLMs, and I’m curious where the HN community draws the line.<p>Suppose you spend months deeply researching a niche topic. You make your own discoveries, structure your own insights, and feed all of this tightly curated, highly specific context into an LLM. You essentially build a custom knowledge base and train the model on your exact mental framework.<p>When you finally want to write a post or comment sharing your findings, you outline your specific thoughts and use that meticulously primed LLM to structure and generate the final prose.<p>My questions for you:<p>Is it unethical to post this without an "AI-generated" disclaimer? 2. Whose knowledge is actually being showcased? The LLM is generating the syntax, but the semantics, the insights, and the deep context are 100% human-sourced.<p>Is this fundamentally different from using a ghostwriter, an editor, or a highly advanced compiler? If I am doing the heavy lifting of context engineering and knowledge discovery, it feels restrictive to say I shouldn't utilize an LLM to structure the final output. Yet, the internet still largely views any AI-generated text as inherently "un-human" or low-effort.<p>Where does human insight end and AI generation begin? If the core ideas are yours, is the medium of the text really the message?