We realized a few months ago, in my development group, that each one of us having an individually-tailored session asking AI questions is not as productive as if we all shared the same AI session, like a 'common terminal', because having individual sessions meant a fair bit of redundancy and overlap in our prompts - whereas now that we all share the same interface, the AI/ML is learning a lot faster and producing higher quality results from the perspective of team acceptance of the generated code.

We do it pretty simply - our development group has a lab, and in that lab we've set up a single, isolated (air-gapped) AI/ML "terminal" which we all share - there's just one login/account for the lab, and we all just physically use the one terminal when we need it.

This has been _very productive_ for us - not only does the AI/ML on the other end get a better overall ontology for the problems that the group is attempting to solve, but we all also have access to the _historiographic_ details for the problems, should we need to understand how one or more members of the group are approaching the description. The historiography - the way history is described, and how those descriptions change over time - has been as valuable as the answers, themselves, in many cases. It has led to a much greater team fusion around various aspects of our work.

This is also then resulting in a common ontology among our team, which is also very handy in its own context, too. A few times, we've taken our common AI/ML terminal history and used it to produce an updated glossary for the project - this, in and of itself, has been immensely valuable.

Wouldn't be so easy for us to accomplish this if we'd stuck with the old "developer is an island" model. Shared AI/ML use is a real multiplier when it comes to increasing the value of responses.

Needless to say that this approach requires a healthy dose of maturity, individual hygiene, and trust between the team members.