AI and the Law: What Large Language Models Mean for Legal Work

When people talk about using AI in law, what they almost always mean—whether they realize it or not—is large language models (LLMs). From legal research tools to contract automation platforms, LLMs are quickly becoming the core engine behind many new legal tech solutions.

But, to make smart decisions about how to use AI in your legal practice, it helps to understand what these models actually do, and what they don’t.

In a recent Synaptiq webinar, Dr. Tim Oates, Co-founder and Chief Data Scientist, helped define how Large Language Models can be used effectively in the field of law.

What Exactly Is an LLM?

There is one basic function LLMs are designed to do: predict the next word in a sequence of text.

That may sound simple, but when you scale it to models like GPT-4, which is estimated to have over 1.8 trillion parameters and is trained on trillions of words, the result is a system that can generate coherent, context-aware text. These models have learned patterns of language, reasoning, and even domain-specific knowledge—including law—by analyzing massive amounts of publicly available data.

Understanding this is important: LLMs don’t “think” like humans. They generate language based on probability. With the right guardrails, prompts, and human oversight, that probabilistic engine can be a powerful tool for the legal profession.

Understanding the Legal Impact of LLMs

Legal work is mainly built around two things:

1. Text — contracts, statutes, discovery documents, case law, regulatory filings

2. Reasoning — interpreting laws, weighing arguments, applying facts to rules

LLMs excel at handling large volumes of text-based tasks such as summarizing, classifying, extracting data, answering questions, and drafting.

But, when it comes to reasoning, it becomes more nuanced. While LLMs can mimic logical thought, they may struggle with complex, step-by-step logic or ambiguous scenarios—especially where precision is critical. In legal work, this means you should always validate outputs, especially if they’ll be used in client-facing or court-facing documents.

Real-World Applications

Here are a few areas where LLMs are already reshaping legal work:

1. Contract Analysis and Drafting

• Summarizing lengthy agreements

• Extracting structured data (e.g., parties, obligations, timelines)

• Using existing contracts as templates to generate new ones

2. Legal Research

• Summarizing court opinions

• Comparing fact patterns

• Identifying legal arguments across similar cases

• Drafting memos and briefs

3. Automation of Administrative Work using Chatbots and AI Assistants

• Client intake and triage

• Providing basic legal information (with disclaimers)

• Internal tools for billing, training, or knowledge management

Enhancing Immigration Law with AI

Synaptiq recently helped an employment-based immigration law firm streamline their document management and processes. Their current process required reviewing dozens of client documents, including passports, resumes, government forms, all done manually. It was slow and error-prone.

We implemented an AI system that used OCR, computer vision, and language models that automatically classified and validated documents. This significantly reduced manual review time and improved the client experience.

That solution is still in use today, and we’re now exploring upgrades using newer multimodal language models that can handle text and images together, further streamlining the workflow.

Tips for Using AI

When implementing LLMs in a legal setting, a few critical issues need to be considered:

• Document Size: Long documents often need to be “chunked” for processing.

• Source vs. Background Knowledge: LLMs blend document-specific facts with what they’ve learned during training—understanding the difference is crucial.

• Accuracy: Always “trust but verify.” Use citations and ensure outputs are reviewed by qualified humans.

• Privacy & Security: Be selective about where sensitive legal documents are processed.

• Bias & Guardrails: Models can reflect societal or training data biases. Use disclaimers, add oversight, and ensure escalation paths to human experts.

Final Thoughts: Start Smart, Move Fast

LLMs are not lawyers, but they can be powerful legal assistants, especially for high-volume, text-heavy, or repetitive tasks. If you’re considering adopting AI in your legal practice, start by asking:

• What are our most time-consuming processes today?

• Where is accuracy mission-critical, and where is speed more important?

• Do we need an off-the-shelf tool, or should we build something tailored to our workflows?

AI is evolving quickly and sitting on the sidelines is no longer a strategy. Now is the time to experiment, validate, and learn by doing.

Let’s Chat. Contact me if you're exploring how AI can improve your legal workflows. I’m happy to share what we've learned.