Trying to get AI to slow down and think through problems with me was a puzzle. What made the difference was developing what I call an AI Collaboration Identity - a systematic framework that combines prompt engineering with context management that maintains collaborative patterns across conversations.

The Challenge

I needed an AI that would reliably engage in collaborative thinking tuned to me, and I discovered that certain language patterns consistently activated this behavior.

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?

I spent last week in Boston visiting one of our Amazon Robotics locations, and it got me thinking about something I heard last year at a Robotics conference that really stuck with me: “Not everyone in robotics is a roboticist.” To me, that meant, “You can be valuable here.”

Jumping into this new domain last year, I was impressed by the depth in each field that it takes to make robotics work. As Software Engineers, we generally learn a domain to apply our craft to. For example, in the past I’ve learned about how hotel reservation and travel systems work, how trains work, etc. Robotics is not a single domain. I quickly realized just how interdisciplinary it is, and that it’s probably the most diverse field I’ve worked in so far. Every day, I team up with people from all sorts of backgrounds: hardware and mechanical engineering, electrical engineering, network engineering, systems engineering, research and applied sciences, computer vision and AI domains, industrial design, and, of course, software engineering (my own background).