When accurate AI is still dangerously incomplete

Typically, when building, training and deploying AI, enterprises prioritize accuracy. And that, no doubt, is important; but in highly complex, nuanced industries like law, accuracy alone isn’t enough. Higher stakes mean higher standards: Models outputs must be assessed for relevancy, authority, citation accuracy and hallucination rates. To tackle this immense task, LexisNexis has evolved beyond standard retrieval-augmented generation (RAG) to graph RAG and agentic graphs; it has also built out "planner" and "reflection" AI agents that parse requests and criticize their own outputs. “There’s no such [thing] as ‘perfect AI’ because you never get 100% accuracy or 100% relevancy, especially in complex, high stake domains like legal,” Min Chen, LexisNexis' SVP and chief AI officer, acknowledges