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I think for me, it’s the idea that if you have a deep level of professional knowledge in a given area you can train AI to use / prioritise sources that you really trust or that you know are really achieving results. I learned this recently from some content on using AI with Click Funnels by Russell Brunson (RB). As the co-founder of Click Funnels, he can see how much money each funnel is making. He has spent many hours over the last few months curating the best examples (e.g. the highest converting social media ads and funnel pages) and copying only the best ones into a closed/offline document library (what he calls his AI brain) for Claude to search. So now when he uses Claude to create sales copy or ads etc for a new funnel, he restricts Claude to only searching his own document library because he knows these funnels have hugely outperformed industry averages instead of diluting the AI results by allowing Claude to have free reign to search any/all sales funnels thst are in the public domain. Brunson’s own data and metrics and knowledge of what converts are far superior to the industry average or even the “average” Click Funnels user, which will obviously include lots of beginners who really don’t know what they are doing and have launched funnels that don'tactually work yet. We’ve all heard the computer programming principle… “garbage in, garbage out” but what I like about what RB has done with AI is that he is making it much smarter because RB's human brain and deep professional knowledge has engaged in the task first rather than just starting with a generic AI prompt. I guess Lucy this is potentially an example of your “think, prompt, think” process in action ;)