In production, hallucinations are less a model issue and more a system design problem.

Relying on the model alone to “be correct” doesn’t scale. What works better is building layers around it.

A few approaches that tend to hold up in practice:

• Constrain outputs using retrieval or grounded data instead of open generation

• Confidence and consistency checks, especially for critical responses

• Post-generation validation, where outputs are verified against rules or sources

• Human-in-the-loop for high-risk decisions

• Monitoring patterns over time, not just individual responses

The shift is from trying to eliminate hallucinations to detecting and containing them early.

In real systems, it’s less about perfect answers and more about controlled failure modes.