Our summary
A support assistant needs to do more than write convincing replies. Teddy Lee's Korean tutorial separates choosing an action from writing its details. A sample shop makes those decisions visible, so learners can see why the assistant checks delivery, answers a policy question or asks a person about a refund.
Jev chooses from a fixed tool list, then the language model writes the needed details and reply. Uncertain choices leave the full shop-tool list available. Refund and cancellation requests get another check; uncertain ones pause for approval. Other lessons cover message checks, shortening stored conversations, search choices and selecting browser actions.
This is a teaching shop with data kept in server memory, not a production store or permission system. Its cutoffs are examples, not proof that an action is authorized. Pattern-based masking misses names and addresses; original saved messages, tool-result privacy and the final streamed reply also have limits. Hosted model calls cost money.
Key takeaways
- Separate the choice of a tool from writing its details. Show the probabilities so a learner can inspect the decision.
- Keep uncertain action choices on a fallback path, and ask a person when the example's refund or cancellation gate cannot decide.
- Treat message checks as limited checks, not complete privacy protection. A model's answer cannot replace customer permissions or a real store's rules.