How context quality, retrieval boundaries, sensitive data rules, and evaluation shape whether AI output can be trusted.
Why this note exists.
AI reliability is shaped by the data environment around it: what sources are allowed, what context is missing, and what output can be reviewed.
Observations
- Source quality and retrieval boundaries change answer reliability.
- Sensitive data rules must be designed before launch.
- Evaluation should test wrong, missing, and partial context.
What we learned
- Define approved sources and exclusion zones.
- Use evaluation cases from real workflow examples.
- Put human review where uncertainty remains high.
Useful artifact
- Workflow map or decision note.
- Test case, approval log, or delivery handoff.
- Reusable pattern for a future service path.