The solution is building your own tracking system using no-code automation tools. This approach requires more initial setup but provides ongoing monitoring at a fraction of commercial tool costs. The system I built uses Make.com, a no-code automation platform, to query AI models systematically, analyze responses, and track mentions over time. Make offers 1,000 operations monthly on their free tier, making it possible to start tracking without any monetary investment.
“혁명수비대 업은 강경파” vs “빈살만식 개혁 가능”…하메네이 차남 엇갈린 평가
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В КСИР выступили с жестким обращением к США и Израилю22:46
Путин внес изменения в особый порядок получения гражданства в новых регионахПутин сделал бессрочным особый порядок получения гражданства в новых регионах
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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.。业内人士推荐体育直播作为进阶阅读
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