In production,\u00a0hallucinations don\u2019t show up as errors: they show up as responses people initially trust.\u00a0This initial trust can be costly, however.\u00a0What we\u2019re seeing across real deployments is that hallucinations aren\u2019t a single bug to fix. They\u2019re a system-level behavior that emerges when a few things go wrong together:They don\u2019t 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\u2019t 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\u2019t an edge case; they\u2019re 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\u2019re looking to save yourself from costly errors, then absolutely.Read the full analysis <\/p>\n","protected":false},"excerpt":{"rendered":"