A Local Model Workflow Audit
Local AI work gets stronger when the machine setup, data boundary, and review loop are written down instead of living in memory.
LOCALOPTIONAL
CLOUD
Three takeaways.
Keep the useful core visible and under the user's control.
Map the boundary
Start by naming what data should stay on the machine and what, if anything, can safely leave. The local boundary should be visible to the person using the workflow.
Confirm what should stay local.
Make runs repeatable
Record model version, prompt, parameters, folder pattern, input format, and expected output. Without that trail, local runs are hard to compare and harder to trust.
Record the model and settings.
Review the output path
The audit should end with the human action: approve, reject, revise, export, summarize, or queue. Local output becomes useful only when it lands in a decision.
Turn outputs into reviewable decisions.
Local AI work gets stronger when the machine setup, data boundary, and review loop are written down instead of living in memory.