Building a Hallucination Detection Pipeline for Production LLMs
Your LLM application passes every eval. The demo looks flawless. Then a user asks about a niche regulatory requirement and the model confidently cites a statute that doesn't exist. The support ticket lands in your inbox twelve hours later, long after the fabricated answer has been forwarded to a compliance team. This is the hallucination problem in production: not that models get things wrong, but that they get things wrong with the same fluency and confidence as when they get things right.
Most teams treat hallucination as a prompting problem — add more context, tune the temperature, tell the model to "only use provided information." These measures help, but they don't solve the fundamental issue. Post-hoc verification — checking claims after generation rather than hoping the model won't make them — is cheaper, more reliable, and composes better with existing infrastructure than any prevention-only strategy.
