Treating your AI tool like a tool makes it act like one. But when you treat it like a competent collaborator, it responds more like one. I started experimenting with this while using Claude Sonnet 4.
What if it could learn from our work together the way a person might and recognize patterns in how I think through complex problems?
Capturing the Learning Journey
To explore I built feedback loops that captured our learning journey, not just what we learned but how our collaboration evolved: the aha moments, the shifts in thinking, not only the final insights. I tested both distilled summaries and the full chronological record. The journey approach created much greater processing depth.
It makes sense when you think about it. When you treat AI like a collaborator, you naturally share more context and reasoning. That gives it more to work with and more material for the learning process. Similarly, if you talk to a person like a tool, they won’t be in a good position to help either.
What Changed
The results were intriguing, and genuinely exciting! Instead of just executing tasks, it began responding with better contextual awareness - connecting ideas across workstreams and even giving more critical feedback without being explicitly asked to do so, and asking more relevant follow-up questions.
Getting Started
If you’re curious to try, start by asking your AI to keep a markdown log of what it’s learning and load it back into the next session - that small step can start to change the interaction.
This was just the beginning, and I’ll share more soon about how these collaboration patterns continue to evolve.