Avoid Hallucination by Breaking Up Prompts

Cramming multiple instructions into one prompt leads to higher AI hallucination rates. The solution is to write clear, single-objective prompts and add structure to constrain the model's output.

Details

Language models are next-token predictors, so when given a single, well-scoped task, the probability distribution for the next token is relatively narrow. But stacking multiple tasks into one prompt triples the surface area for error, as the model has to maintain coherence across objectives simultaneously. Research shows longer, multi-part prompts increase error rates by 10%. The solution is to write one prompt per task, add structure like examples and output formats, and give the model permission to refuse if it's not confident. This disciplined approach is more effective than sophisticated prompt engineering frameworks.