Why Solving Legal AI's Context Problem Is Harder Than You Think
Legal AI's primary challenge is not model intelligence, but the difficulty of providing accurate, governed, and deeply connected professional context.Summary
Dan Hauck, Chief Product Officer at NetDocuments, argues that the frustration lawyers feel with AI stems from a "context problem" rather than a lack of model intelligence. While AI models are becoming more capable, they often fail because they lack access to the nuanced matter history, precedents, and specific connections that constitute the actual work of law. Simply increasing the volume of documents provided to a model is insufficient; instead, the industry requires "context engineering" and the development of context graphs that understand the "why" behind legal decisions.
A critical component of this challenge is the inseparable link between context and governance. In the legal field, AI must operate within strict ethical walls, client restrictions, and jurisdictional requirements. If an AI tool creates independent copies of data, maintaining synchronization with these shifting permissions becomes an engineering nightmare. The solution lies in building AI capabilities directly into the "system of record" where permissions and policies are already managed, ensuring that context is delivered securely and in real time.
Ultimately, solving the context problem through robust engineering will transform legal workflows. Rather than replacing human judgment, a context-aware AI allows associates to start from a foundation of institutional knowledge rather than a blank page, and enables AI agents to draft documents based on a firm's specific expertise. The winners in the legal AI race will not be those with the largest models, but those who successfully master the complex layer of governed, institutional context.
(Source：Forbes)
