Building AI Systems That Survive Production
A scaffold for notes on the gap between promising prototypes and AI systems that hold up under real users, policies, failures, and operational pressure.
Thesis
The hard part of AI product work is not producing one good answer; it is making the system reliable across messy inputs, changing policies, and operational constraints.
Context
- Prototype success criteria.
- Production success criteria.
- Failure modes that only appear with real users.
System Design Notes
- Make model calls one part of a larger workflow.
- Capture inputs, decisions, outputs, and downstream effects.
- Design fallback paths for uncertainty and failure.
- Add observability around model behavior and business outcomes.
Evals and Operations
- Start with representative cases.
- Track regressions as prompts, tools, and models change.
- Combine automated checks with human review.
Tradeoffs
- Coverage versus cost.
- Latency versus verification.
- Automation versus escalation.
Conclusion
Production AI systems survive by combining model capability with ordinary engineering discipline.