AI’s Legal Blind Spot: Why Generic LLMs Struggle with Legal Nuance
This blog post is for informational purposes only and does not constitute legal advice. For specific legal concerns, please consult a qualified attorney.
Introduction
Large language models (LLMs) are already shaking up knowledge work, and given the notoriously high costs of legal services, lawyers and legal departments are particularly eager to harness these tools. Recent surveys found that 34% of lawyers already use generative AI, and around 17.5% are using it “in production on live matters.” By late 2024, a solid majority of in-house professionals (76%) and law firm attorneys (68%) reported using LLMs at least weekly.
When I talk to colleagues about AI implementation, two major themes consistently emerge.
The first centers on trust and safety: ethical considerations, data privacy, confidentiality, and preventing sensitive information from entering model training sets. For an in-depth exploration of these critical issues, see our companion piece here.
The second theme—and the focus of this post—involves the performance and reliability gap between what these AI tools offer and what lawyers actually need. There’s a sense that generic LLMs don’t quite hit the mark (a friend calls them “dancing bears”, entertaining but not exactly reliable). I call this AI’s “Legal Blind Spot.”
Out of the box, even advanced LLMs struggle with the precision and nuance of legal tasks. Below, I dive into why this blind spot exists, how it affects legal departments, and what we can do to make these tools more useful for lawyers.
Definition
AI’s Legal Blind Spot is the gap between the general-purpose LLM capabilities and the specialized, context-dependent nature of legal work. I see this most often with contract analysis. LLMs are wizards at pattern recognition in massive datasets, which is great, but legal analysis also requires a nuanced grasp of client-specific language, context, and priorities.
Consider liability limits in contracts. A generic LLM can parse language, but it won’t understand how a client’s past litigation shapes their risk tolerance. It might also get bogged down analyzing irrelevant clauses (e.g., overanalyzing data protection for a client whose products don’t touch personal data). Research shows that generic LLMs can lack “rigor and precision” when handling legal complexity.
Human lawyers learn the difference between the “right” answer and what truly matters through trial and error—often in conversation (or a performance review) with other lawyers. Over time, they develop the judgment to ignore irrelevant details and zero in on what matters most to the client in the situation at hand.
In my experience, LLMs aren’t so different from law students or even experienced new hires. They’ve studied the textbooks, or had success in practice, but haven’t yet learned what to ignore or how to prioritize in real-world scenarios for a specific client. Without tailored context, LLMs can produce outputs that are incomplete or outright wrong.
Causes
So why the struggle? Several factors are likely at play:
Limited Attention (aka “The Context Window”): LLMs can only handle a fixed amount of information at once—roughly 128k tokens (~96 pages). Practically, quality dips sharply after about 20 pages of context, leading to less relevant or incorrect outputs.
Lack of domain expertise at tech companies: Unlike domains with clearly right or wrong answers, law is characterized by “multi-level hierarchies, domain-specific vocabulary, and nuanced interpretations” that “pose significant challenges” for LLMs. Most AI companies are experts in tech, not law. Even if they hire lawyers, those lawyers are ethically required to prioritize the tech company's interests. This means generic legal playbooks that don’t necessarily account for the client’s unique risks or priorities.
Chaotic Legal Data: Legal departments generate tons of unstructured data—emails, endless redlines, messy drafts—making it challenging for AI to identify what's truly important.
Implicit Knowledge: Experienced lawyers know how to negotiate with certain parties or why specific terms are deal-breakers, yet these insights are rarely documented clearly enough for AI to use.
Shallow Data Structure: Current solutions, like basic legal playbooks, rarely capture the full context behind policy decisions, leaving LLMs stuck working with fragments.
Impacts
This blind spot can create real headaches:
Inaccurate Analysis: Without proper context, LLMs might misinterpret clauses, exaggerate minor points, or miss key risks altogether.
Loss of Trust: Lawyers quickly lose trust if the AI repeatedly gets details wrong, undermining the entire investment.
Wasted Resources: Teams may pour effort into testing an AI tool, only to find it needs constant fixes. Instead of saving time, it adds work.
Missed Opportunities: The dream of AI is to free lawyers for strategic tasks. But if the tool can’t handle the basics, that promise fades.
Reducing the blind spot is necessary if AI is going to be a real asset rather than a burden.
Solutions
Fortunately there are strategies which can make LLMs more useful in legal work.
1. Structured Data
LLMs perform best with clear, organized input. By investing in structured legal data (e.g., client preferences, standardized clauses, critical business context) we create reusable, high-quality foundation data. Think of it as giving a student a carefully curated reading list instead of unleashing them on the entire library.
After hundreds of hours working with multiple generations of these tools for a wide variety of tasks, I have come to believe that no matter how advanced AI gets, it'll always need help distinguishing between what's relevant and what isn't. This ultimately comes down to human preference, and in essence foundation data is a tidy bundle of distilled, top shelf human preferences.
2. Legal Reasoning Frameworks
Legal analysis requires interpreting and applying rules to specific situations. Structured reasoning frameworks are layers on top of structured foundation data which add the “why” behind legal decisions, moving beyond conclusory statements like “we don't waive consequential damages” to the underlying rationale (e.g., specific risks tied to the client’s unique business).
Together, structured data and reasoning frameworks can help LLMs distinguish genuine issues from boilerplate concerns more like a lawyer would.
3. Knowledge Representation
There are solid reasons to believe that emerging tools will further improve AI performance in the near future.
GraphRAG: Maps relationships between legal concepts, with the aim of improving retrieval and reasoning over complex context.
Model Context Protocol (MCP): Standardizes how LLMs access and integrate relevant data sources (e.g., case law, internal memos, contract repositories) to make retrieval seamless (without custom integration each time).
Over time, these tools may help LLMs access precisely the context needed, reducing the blind spot by automatically directing them to the right “books” in the library.
Things to Consider
If you’re exploring LLMs, here are some suggestions which may improve the LLM’s performance and your experience:
Invest in data structuring: Garbage in, garbage out. Consider spending time upfront curating and organizing legal data (e.g., contract forms, playbooks, preferred redlines) and relevant business information (e.g., business purpose, resource constraints) relevant to your use case. This is a library of high-quality context you can add to LLM prompts or project knowledge directly, or convert into a structured format to support legal reasoning and evaluation frameworks. It’s the foundation for everything else.
Evaluate the use case: Recurring, high volume work benefits from foundation data. One-offs may not. Look at where your team spends the most time. ACC (Association of Corporate Counsel) polled in-house counsel who identified legal research, drafting/reviewing contracts (especially NDAs), summarizing court decisions, and preparing legal charts as areas where LLMs are currently most helpful.
Keep humans in the loop: It goes without saying, but even the most sophisticated models are not lawyers and can miss in very subtle ways with potentially significant consequences.
Understand the two-way relationship: Output hinges on input quality and model limits. Overloading an LLM with unrelated tasks (like cross-checking references and identifying risks) or too much context muddies the waters.
The Legal Blind Spot is real, but not insurmountable. With thoughtful strategies and a bit of humility about what AI can and can't do, legal teams can position AI to transform from a dancing bear into a trusted assistant.
Thanks for reading, and may you be well.
Jace
