Deployment, privacy and responsible AI
The checklist for shipping AI without losing secrets, data, money, or user trust.
In plain terms
You need to know where user data goes, how long it is stored, what the model is allowed to say, and what happens when it is wrong.
Why it matters
AI systems can be fluent and wrong, can expose private data through providers or logs, and can create real-world harm in sensitive domains.
How it works
Use environment variables, CI checks, eval gates, PII handling, deletion paths, provider data policies, and clear human review for consequential decisions.
When you use it
Before real users, and especially in health, finance, legal, education, or child-facing products.
Common mistakes
- Not reading provider retention policies.
- Deleting an account but leaving embeddings and traces behind.
- Using confident copy in domains that require caution.
Best practices
- Draw a one-page data flow map.
- Add harmful-advice and bias evals.
- Make evals pass before deploy.
Try it yourself
Map every place user text travels in your app and remove one unnecessary storage location.