Hallucinations in AI models, especially in document automation, pose significant risks by producing incorrect data. Using human oversight, validation rules, and Small Language Models (SLMs) can mitigate these risks. Strategies include implementing strong and weak grounding to ensure AI outputs are accurate, and breaking down complex tasks to reduce opportunities for hallucinations.
Table of contents
Using validation rules and “human in the loop”Small Language ModelsRisk tolerance and GroundingGrounding for complex problemsKey TakeawaysSort: