Writing with LLM is like doing a mapping from human prompt -> document. The longer the prompt, the closer the output document to the human intent. If the prompt is short (= human intent is low), the output must be filled with fluff, generated from the general knowledge/common sense in LLMs knowledge-base. If the prompt is very detailed, it's not much different than writing it yourself. Somewhere between these two extreme cases, writing with LLMs have the optimal productivity gains.

-> (GPT 4.5 "rewrite like a good editor")

Writing with an LLM can be viewed as translating human intent—expressed via a prompt—into a written document. The longer and more detailed the prompt, the closer the resulting document aligns with the original intent. Shorter prompts, indicating lower clarity or precision, compel the LLM to rely on general knowledge or common sense to fill in content, often resulting in fluff. On the other hand, excessively detailed prompts become nearly equivalent to writing the document yourself. Optimal productivity with LLMs occurs somewhere between these two extremes.