ZKPs is a method that allows a person—looking to verify themselves—to answer statements in a manner that establishes trust to the verifying party without unveiling personal or secret information. Take, for example, the problem of 4+4=8. This is something the person looking to be verified knows to be true, but the ZKP method relies on trust. Instead of asking is 4+4=8, the verifier asks a series of questions to determine if the person wanting to be verified is telling the truth (or in this case, knows that to be true). The verifier can ask is 4+4=7; is the sum of 4+4 an even number, and so on and so forth, and after the series of questions, it can determine the veracity of the person’s claims, thereby identifying them.
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standard, for some input program. YARPGen has (in effect) a built-in
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。