In production, hallucinations don’t show up as errors: they show up as responses people initially trust. This initial trust can be costly, however. What we’re seeing across real deployments is that hallucinations aren’t a single bug to fix. They’re a system-level behavior that emerges when a few things go wrong together:They don’t originate in the model alone. Tool selection, retrieval quality, prompting, and orchestration logic can all amplify small uncertainties into confident falsehoods.They slip past standard monitoring. Accuracy metrics miss most hallucinations. Signals like uncertainty, grounding gaps, tool failures, and confidence mismatches often surface only after users notice.They compound with feedback and scale. When corrections aren’t captured (or are misread as preferences), hallucinations reinforce themselves. Increased usage then exposes edge cases that testing never revealed.If your safeguards live in prompts instead of system design, hallucinations aren’t an edge case; they’re inevitable.Our recent article by Maria Piterberg breaks down why AI hallucinations happen in real systems, and what mature teams do differently to contain them.Worth a read before the next scale-up? If you’re looking to save yourself from costly errors, then absolutely.Read the full analysis