Most “hallucinations” aren’t random.
They’re what happens when you leave a gap and ask a system built to complete patterns to keep going anyway.
LLMs are not shy about uncertainty. They will produce a clean, fluent answer that sits right on top of missing context. Not because they’re trying to deceive you, but because the engine is optimized to continue the most likely sequence.
If you want reliability, your job is not “tell it to stop hallucinating.”
Your job is to make guessing expensive and stopping easy.
Simple guardrails that help
- Ask for uncertainty up front:
List what you would need to know to be confident. - Force a boundary:
If information is missing, say 'INSUFFICIENT INFO' and stop. - Separate facts from inferences:
Two sections: Verified vs Assumptions. - Require sources when it matters:
Cite where each claim comes from.
The mindset shift
When the output is wrong, don’t only blame the model.
First ask: Did I leave it room to guess?