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.
英国工程咨询公司Arup香港团队在深伪视频会议中被冒充的高管指示转账，损失约2500万美元，涉及多次转账与多个账户。这个事件到底算欺诈险，还是算网络险。欺诈险视角强调员工被诱导转账，网络险视角强调数字身份被盗用、流程被操控、取证与恢复成本高企，以及可能产生的第三方索赔链条。路透社报道指出这种灰区造成的行业后果是当同一事故可以被解释为不同险种、不同触发条件、不同除外责任时，保险公司会更倾向于提高费率、收紧承保条件、甚至在续保时要求企业部署更强的身份验证与深伪检测工具。
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