Seconding this request... Our team is going through AI adoption without a clear direction and we're getting to the point we need to sit and come up with some guidelines. It would be awesome to have some human-made examples as inspiration
Here are the rest. I wrote these to specifically be principles that can be easily internalized and pointed to for accountability:
2. Own the Output. Everyone is encouraged to use these tools to accelerate the pace of their work and to take the toil out of their daily output. But that doesn’t mean you simply defer to the model and skip your own careful review step. Part of the paradox of these models is that they’re wordy and it can feel exhausting to read through everything they’re writing and confirm its validity. But that’s an enormous part of the job: you’re the human-in-the-loop to make sure what it’s saying is correct, and that you’ve reviewed it before you’ve shared it with the rest of the class. Read all of the words before they reach the rest of your team or a client, and make sure you stand behind having your name attached to them. In situations where a full review isn’t possible for expediency, disclose it directly! “Hey, I haven’t had a chance to review, this is pure Claude, but here’s what it says.” This isn’t a get out of jail free card, but it can be used occasionally when the situation dictates. Most of the time, you need to review and adjust the output before other people see it.
3. Be the expert. Have an opinion. This is a big one. Do not offload your role as expert to the model. The model is an assistant. You need to internalize the work being asked of you, and apply your education, instincts, and specific expertise to form your own opinion. You can use models to work to adversarially challenge things, but part of being an expert is knowing when to discard irrelevant feedback. Use this skill daily. Don’t let it atrophy.
4. Don’t Slop-Bomb. This is an amalgamation of all of the above, but again, don’t just spew large volumes of model slop into comms channels. Bring your own perspective, internalize the problem in front of you. And be judicious about what you share, and how wordy these things are. Put another way: edit for clarity and concision. It’s too easy to generate busy-work-seeming reports and outputs, and that burden shift to everyone else is multiplicative. Use fewer words, distill things into insights (backed with longer form deep dives), but make sure you’re not just making the rest of the team read a bunch of useless pablum. I don’t want to have to make my model read your model’s output to glean what I’m supposed to out of it. Do that for your coworkers and your clients. Think in terms of information hierarchy.
