Building Trust: Why Domain Expertise Matters in Legal AI

The legal profession stands at an inflection point. Generative AI tools have moved from the experimental phase to vital research assistants in short order. Legal professionals can now search large databases conversationally, generate draft documents in seconds, and receive AI-powered summaries of complex case law. These are impressive advancements, to be sure. However, many legal professionals know that the beauty of AI tools must run more than skin-deep. It’s ultimately the quality of the underlying content that will prove to be the fundamental differentiator between a genuinely impactful AI solution, and a more surface-level AI assistant.  

It’s important to establish from the jump that the stakes surrounding reliable AI in the legal industry are fundamentally different from those facing applications oriented towards more general consumers. Attorneys need to provide accurate citations that courts will accept. Compliance officers need to properly interpret regulations or risk significant penalties. An AI tool that generates citations to cases that don’t exist or mischaracterizes legal holdings doesn’t just create inconvenience, it creates potential malpractice exposure. “Mostly right” isn’t good enough. It’s the guiding principle that should chart every legal team’s approach to leveraging AI solutions.  

This all begins with content. Some AI solutions feature large language models that have been trained on broad internet content. Outputs are then guided towards legal accuracy either through prompt engineering or additional fine-tuning. While this approach may look good on the surface, consistency and reliability often become moving targets. It likely won’t surprise anyone to hear that people – and AI – have be careful what they read on the Internet: Legal content sourced from the web can be riddled with outdated information, insufficient analysis, and unreliable sources. 

A more tenable approach begins with curated legal content from established publishers and institutional sources that has undergone extensive editorial review before getting anywhere near an AI algorithm. Look for primary law rooted in official sources, expert analysis penned by recognized legal authorities, and procedurally published case law originating from verified court records. 

However, also remember that the value of legal content is measured in more than sheer volume. Domain expertise is a vital component in effectively utilizing legal source material to create viable AI outputs.  Incorporating scholars and editors into the process ensures a deep understanding is present surrounding the information legal professionals need, their workflows, and methods for evaluating the reliability of sources. Arbitrators require different outputs than litigators, and corporate counsel needs different analysis than law firm associates. 

One of the areas where we see the urgency for this caliber of expertise reflected most acutely in legal research and international arbitration. Professionals need to find cases containing specific keywords. But they also need to understand how different arbitral institutions apply procedural rules, in addition to how various jurisdictions approach substantive legal questions. While a generic AI tool may be able to find documents containing the word “arbitration,” it could struggle to incorporate the layers of context and understanding required to meaningfully advance the research process, and lead to more impactful return on investment 

Further complicating matters is that not all legal authorities operating in arbitration or elsewhere carry equal weight. Published opinions in a controlling jurisdiction, for example, carry greater import than unpublished trial court decisions from other jurisdictions. Summarized or paraphrased legal content also threatens to obscure critical nuances that can change case outcomes.  Generating effective AI outputs based on that content requires a careful evaluation of source hierarchy and precedential value.  

Expert-curated taxonomies, practice area classifications, and editorial enhancements must be created in order to help AI systems understand relationships between authorities that aren’t apparent from text analysis alone. The best of these solutions leverage retrieval systems that understand legal citation formats and jurisdictional hierarchies. This requires the creation of summarization capabilities that preserve critical legal distinctions rather than oversimplifying complex guidelines. 

Seek out AI platforms or solutions that offer clear source citations for every factual assertion and legal proposition generated. Make sure that you can understand and evaluate the reasoning at play with full transparency. The goal is to pinpoint sound legal reasoning that allows legal professionals to move forward with confidence.  

The upfront investment of time required to properly evaluate a legal AI solution will pay dividends in superior outcomes that generic approaches cannot match. Verifying a strong foundation of authoritative legal content and the regular involvement of domain experts offers a strong indicator of success, in addition to security for stakeholders. This is something that will only continue to grow in importance as the rise of generative and agentic AI solutions confronts the legal industry with both unparalleled opportunity and the responsibility that follows in its wake. As the legal industry navigates this transformation, the most valuable AI solutions will be those that enhance professional capabilities without compromising the judgment, accuracy, and professional responsibility that define competent legal practice.