人們嘗試過各種奇葩策略，試圖從大型語言模型（LLM，ChatGPT等工具背後的AI技術）中獲得更好的回饋。有些人深信，威脅AI能讓它表現得更好；另一些人認為，禮貌待人會讓聊天機器人更配合；還有些人甚至要求機器人扮演某個研究領域的專家來回答問題。這樣的例子不勝枚舉。這都是圍繞著「提示工程」或「情境工程」——即建構指令以使AI提供更佳結果的不同方法——所形成的迷思的一部分。但事實是：專家告訴我，許多被廣泛接受的提示技巧根本不起作用，有些甚至可能是危險的。但是，你與AI的溝通方式確實至關重要，某些技巧真的能帶來差異。
There was a thread on mastodon recently where Conor Mcbride was discussing some really cool stuff. I think the intent was to get at something more interesting, but this is the first place I’ve seen thinnings explained in a way without too many complex trappings around and it really clicked with me. I think I had a primed mind to see something useful to my current set of problems and solutions there. It unlocked a torrent of ideas related to lambda egraphs and generalized unions finds that I’m quite excited to talk about in the next couple posts.
，更多细节参见wps
"It's the early days and they're still showing this in small numbers at the moment.，详情可参考手游
S$105 per month，这一点在whatsapp中也有详细论述
In the preface to the English translation, Bourdieu hints at a justification: