循着餐桌上的麦香，视线锁定在黄淮海平原这座“大厨房”。作为我国小麦最大的主产区，全国每10个馒头中，就有9个来自这片土地的馈赠。
Эта новость появилась после того, как Гоял провел неожиданную встречу с министром торговли США Говардом Лютником, которая состоялась в рамках частного визита американского чиновника в Индию.
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Once you've identified target queries, the automated system tests them periodically—daily, weekly, or on whatever schedule makes sense for your monitoring needs. Each test queries the AI model with your specified prompt, captures the response, parses which sources were cited, and records whether your content appeared. Over time, this builds a database showing your visibility trends, how often competitors appear for the same queries, and which topics you're gaining or losing ground on.
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.