Keep AI-Generated Proposals Human-Gated

When you build automation that infers connections from activity logs or transcripts, resist the urge to write its conclusions directly into your source of truth. Let the system propose linkages, and let a human accept only the ones that are real. A surprising fraction of confident-looking matches are near-duplicates, coincidences, or subtly wrong — and the cost of silently polluting your knowledge base is far higher than the cost of a quick review.

A useful discipline is to have the automation operate on metadata only — durations, token counts, filenames, timestamps — rather than reaching into sensitive content. This keeps the inference cheap, keeps private material out of the pipeline, and makes the proposals easy to audit at a glance before anything is committed.

Pair this with a layered data model: keep an immutable raw capture, treat derived notes as regenerable projections of that raw layer, and never cache anything that is signed or expiring. When derived artifacts can always be rebuilt from source, you can accept AI suggestions freely, knowing nothing is load-bearing until a human blesses it.