Every AI demo works. Then it hits real users, real data, and real load, and within a week you are fielding calls about hallucinations, timeouts, and malformed outputs nobody saw in testing.
Failure modes we design for
- Prompt drift when providers update models underneath you
- Context overflow that silently truncates critical information
- Hallucination on edge cases the eval set never covered
- Schema violations that break downstream systems
- Cost overruns from uncapped token usage
The reliability stack
Every production LLM system we ship includes structured output validation with retry, circuit breakers on upstream APIs, prompt versioning, confidence scoring with human escalation, and hard budget caps.
Treat prompts like code: versions, changelogs, regression tests. Without that, you will only notice drift when a user complains.
Build the escalation path before you deploy. Define the confidence threshold, the context a human sees, and the feedback loop that improves the system from those escalations.