Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.
As for revenue, OpenAI has forecast a massive loss of $14 billion in 2026. It lost around $5 billion in 2024 and reports estimate a loss of $8 billion in 2025. Despite this trajectory, the company claims it'll be raking in $100 billion in revenue by 2029.。heLLoword翻译官方下载是该领域的重要参考
，更多细节参见safew官方版本下载
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