A very different approach to attachment extraction in AI tools

When you give an attachment to an AI tool, it does not really know what to extract from it so it just pulls out generic stuff. Unless you specifically tell it what to look for, you get a very surface level output. But here is how I approached this differently. I have built a cognitive map of how you as a user think. The tool already knows what you have captured in the past, what it connected to and why. So now when you upload any attachment, the agents refer to that cognitive context and figure out what is actually worth extracting for you specifically, without you having to say anything. So instead of generic extraction, it is pulling out what is relevant to how you think and what you have been working on. But if you do want to tell it specifically what to look for, your instruction overrides the cognitive context because now it has a clear direction from you. The context still kicks in but after the extraction, to connect what was pulled out to everything else you have captured. Curious what you guys think about this approach.