Lesson 6: Confirmed Policies — teaching the AI your preferences
Every time you approve, edit, or reject a recommendation, you are teaching the system. When a preference shows up consistently, it becomes a Confirmed Policy — a remembered rule the bees apply from then on, so you stop having to correct the same thing.
How the AI learns
- Approving a card as-is is a quiet "yes, more like this."
- Editing a card is the richest signal: it shows the AI your exact preferred outcome, not just a thumbs-down. (This is why Lesson 7 tells you to edit rather than silently reject.)
- Rejecting with a short reason tells it what to avoid and why.
Repeat a pattern — always trimming the rate hike, always routing a certain complaint a certain way — and the learning loop proposes a Confirmed Policy capturing it.
Phrasing something so it sticks
If you already know a rule you want enforced, say it in plain, absolute terms and give the reason:
- "Never price Standard rooms above NPR 9,000 — we lose our regulars."
- "Always inspect a VIP room twice before it's marked sellable."
- "For corporate bookings, don't send marketing SMS — their travel desks handle it."
Words like always, never, and for [this group] signal a hard boundary rather than a one-time request. Compare:
One-time: "Don't raise Standard above 9,000 this weekend."
Policy: "As a rule, never raise Standard above 9,000."
Reviewing and retiring policies
Confirmed policies live in your Autonomy settings. You can read every active policy, edit it, or retire it when your hotel changes — a new season, a renovation, a new room class. Policies are boundaries the AI respects, so keep them current: an out-of-date "never below NPR 2,000" floor can quietly cost you fill on a dead week.