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.
For example, instead of targeting the SEO keyword "WordPress hosting," you'd track the AIO query "What's the best WordPress hosting for SaaS applications?" or "Which hosting provider should I choose for a WordPress-based business site?" These natural language questions better represent how people interact with AI tools and help you optimize for actual usage patterns rather than keyword variations.
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Note however that depending on as well as the distribution of colours in the palette, there may not always be an exact solution for any given . Instead we’ll say that we want to minimise the absolute error between and some linear combination , or .