You can teach a model facts; you teach it wisdom by example.
Machines are good at memorizing. Wisdom is a different craft. Wisdom needs correction, nuance, and the patient repetition of a teacher who says, “No, that word means something else here.” At Zoolch, the most transformative moments came when we stopped treating models like oracles and started treating them like apprentices.
One project involved an AI documentation assistant that drafted discharge summaries. It was fast and technically accurate, but clinicians hated its voice: robotic, incomplete, and missing a patient’s subtle worries. Instead of tweaking thresholds, we instituted a human in the loop cycle. Clinicians annotated drafts, flagged omissions, and suggested better phrasing. We didn’t just retrain weights; we taught the model a social grammar: validate fear before instruction, name barriers before solutions, and close with a small, human sentence that signals presence.
Another example came from a scheduling assistant for a shared office. Early on, it suggested meetings during colleagues’ “quiet hours.” The office manager corrected it once; the agent adapted. But the change wasn’t only a time zone fix; it was a preferences layer: gentle rules (do not schedule earlier than X for person A) and incentives for asking clarifying questions when conflict arises. Over weeks the assistant’s behavior shifted from blunt efficiency to considerate collaboration.
These human corrections did more than reduce errors. They shaped their temperament. The agent learned to prefer asking instead of assuming, to default to conservative options in uncertain contexts, and to accept correction without degrading trust. In product terms, humility reduced costly missteps and increased user trust by measurable margins.
Practical takeaways
- Build annotation flows that make correction frictionless and fast. One click feedback is gold.
- Reward uncertainty in the model (e.g., have it ask instead of decide when confidence is low).
- Treat model updates as cultural training: include domain experts in labeling and iterate in small batches.
- Track not only accuracy but “correction rate” and “user satisfaction after correction.”
Humility turns a clever tool into a steady colleague. Teach it with patience, and the machine will repay you with fewer mistakes and more trust.