Hallucination Mitigation Framework
Definition: A hallucination mitigation framework is a structured way to make sure AI tools give accurate, fact-based answers instead of guessing or making things up. It combines safeguards like using verified sources, human review, and regular testing to keep outputs reliable.
Example
A law firm uses AI to draft contract summaries. Before sending results to clients, the AI system cross-checks every clause against an internal database of real contracts and adds references to the original text. A lawyer then reviews the summary before final delivery.
Why It Matters?
In law, accuracy is everything. If AI produces even one wrong citation or misinterprets a case, it can hurt a client’s position or a lawyer’s credibility. A hallucination mitigation framework helps build trust by ensuring AI tools are used safely and responsibly.
How to Implement?
Start by connecting AI tools to verified document sources rather than letting them rely on memory. Add human review steps to catch errors early. Track which kinds of mistakes happen most often and retrain your prompts or workflows accordingly. Finally, keep an audit trail showing where each piece of information came from so you can explain or defend any AI-assisted output.