The model can be perfect and still fail if staff do not trust or understand the internal chat box.
Explain boundaries
In plain language: what data may be pasted, what must never be pasted, and that answers can be wrong. Link to policy, not to research papers.
Teach by task
Show three workflows only: rewrite email, summarize meeting notes, ask handbook questions. Add more later.
Support path
Who to ping when the service is down, how to report a bad answer, how to request a new prompt template.
Point curious users to community style norms and keep the product home microaione.com in the footer of your internal portal. Tutorials for champions: tutorials.
Adoption is a training problem wrapped around a systems problem. Ship the training with the URL.
Extra practice notes
For "Onboarding Non-Technical Staff to an Internal Local AI Chat", triage feedback into slow, wrong, and confusing. Hold a short weekly pass. See MicroAI One and about. Size models with the VRAM table.
Champions
Train two champions per department who see questions first. They collect failure examples for the tech team.
Fear handling
Some staff fear being replaced; others fear looking dumb. Emphasize augmentation and show the human review step in support workflows.
Accessibility
Ensure the UI works with company SSO and with screen zoom. Non-technical does not mean non-busy—friction kills adoption.
Feedback button
A one-click "bad answer" report with optional note is more valuable than a long survey nobody completes.
More executable detail
For "Onboarding Non-Technical Staff to an Internal Local AI Chat", create a single feedback channel where people report slow, wrong, or confusing behavior. Spend fifteen minutes a week tagging issues as hardware, prompt, permission, or expectation. Action after tagging beats endless model swapping. For orientation links you can cite MicroAI One and the about page.
Write decisions in a short runbook with dates. When something breaks, the first question is what changed; a dated note answers faster than memory. Include links to the exact model files and the ports you exposed. Keep the runbook next to the compose file or service unit so it cannot drift into a personal folder. (lp7-0)
Prefer boring defaults for anything shared. Power users can still override locally, but the shared alias should optimize for predictability. Predictability is what makes local AI feel like infrastructure instead of a hobby that resets every Monday. (lp7-1)
When you evaluate changes, change one variable at a time. If you swap the model, the quant, and the client on the same day, you will not know which change helped. Small experiments compound into a trustworthy stack. (lp7-2)
Published by MicroAI One (本地AI 1号网), a Shenzhen team working on local LLM hardware, software and deployment.