AI features fail in new and surprising ways. These are the guardrails we put around every production LLM system.
Shipping an AI feature is not like shipping a normal feature. The output is probabilistic, the failure modes are unusual, and users will push it in directions you never imagined.
Constrain the surface area
Give the model the smallest possible job. Tight scope, structured outputs, and validation at the boundary prevent the majority of production surprises.
Always have a fallback
Design for the case where the model is wrong or unavailable. Human-in-the-loop approvals and graceful degradation keep the experience trustworthy.
- Validate and type-check model outputs
- Add rate limits and cost controls
- Log prompts and responses for auditing
- Monitor for drift and quality regressions