The distance between an AI demo and production.
Why evaluation sets, untrusted-input boundaries, request tracing, and fallbacks belong in the first architecture discussion for an AI product, not the post-launch backlog.
KEY TAKEAWAYS
- A demo shows that an idea can work; production requires knowing when it fails, who notices, and what happens next.
- Build a labeled evaluation set from real inputs before tuning prompts, and track pass rates per category in CI.
- Treat retrieved documents and tool output as untrusted: scope tools to the user and require approval in proportion to an action's impact.