Hallucinations in LLMs aren’t a model problem alone, they’re a system design problem.

In production, the question isn’t “will hallucinations happen?” They will.
The real question is “how quickly can you detect, contain, and correct them?”

A few approaches we prioritize:

1. Grounding over generation
Use retrieval-based architectures so outputs are tied to verifiable sources, not just model memory. If the answer can’t be traced, it shouldn’t be trusted.

2. Confidence is not accuracy
LLMs sound certain even when they’re wrong. Introduce validation layers, rule checks, or secondary models to verify outputs before they reach users.

3. Output monitoring in production
Track patterns, anomalies, and failure cases. Hallucinations often repeat in subtle ways before becoming visible issues.

4. Human-in-the-loop where it matters
For high-impact decisions, escalation paths are critical. Not everything should be fully automated.

5. Design for traceability
Every output should be explainable, what data it used, what logic was applied, and where uncertainty exists.

At scale, you don’t eliminate hallucinations, you build systems that make them visible, measurable, and manageable.

The companies that win here won’t have perfect models.
They’ll have better control systems around them.