Course 9, lesson 89 of 100, Ages 14+
Shipping AI safely
Monitoring, feedback and improvement
Like I’m 5
Launching an AI app is like opening a new shop. You watch how it's going, listen to customers and keep improving.
The big idea
After launch, real users will try things you never tested. Monitor errors, costs, response times and flagged answers. Let users give feedback with a thumbs up or down and an easy way to report problems.
Roll out changes gradually, keep humans in the loop for high-stakes decisions, and have a plan to pause features if something goes wrong. Protect privacy in logs by removing personal details you don't need.
Examples
- Dashboards: Charts of daily errors, costs and satisfaction.
- Feedback: Thumbs-down answers go into a review queue.
- Gradual rollout: A new prompt goes to 5% of users first.
How it works
- Launch to a small group and monitor closely.
- Collect feedback and review problem cases.
- Fix, re-test and roll out gradually.
Check your understanding
- Why roll out changes to a small group first?
- Options: To catch problems before they affect everyone; To keep it secret; Because servers are small.
Answer: To catch problems before they affect everyone. Gradual rollouts limit the damage from surprises. - What should you do with personal details in logs?
- Options: Remove what you don't need; Publish them; Keep everything forever.
Answer: Remove what you don't need. Minimising data protects users' privacy.
Remember
Monitor, collect feedback, roll out gradually and protect privacy after launch.
Talk about it
What would you watch most closely after launching an AI homework helper?
Go deeper
MLOps and LLMOps practices include observability, A/B testing, canary releases, incident response and data-retention policies. Drift monitoring detects when real inputs change over time.