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Generative AI and the Delivery of Legal Services:

A Law Student Textbook and Workbook for Understanding and Implementing AI in Law

Thomas G. Martin

The LawDroid Press
Vancouver, British Columbia

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© 2025 Thomas G. Martin


All rights reserved. No part of this book may be used to train artificial intelligence systems or reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher.


This book is hosted on an installation of the Writebook platform, created by 37 Signals.




LawDroid Press
Vancouver, British Columbia

Open Endorsements

Praise for Generative AI and the Delivery of Legal Services

“Tom is a main character in the evolving story of AI and law. He is clear, knowledgeable, and engaging.”

— Professor Richard Susskind CBE KC (Hon), author of How to Think About AI

“Tom’s textbook offers a clear and insightful exploration of how generative AI is reshaping legal services. It’s a valuable read for students preparing to enter a rapidly evolving legal landscape. Tom’s dedication to advancing legal innovation and education is evident throughout his work, making this textbook a valuable resource for aspiring legal professionals.”

— Gabriel H. Teninbaum, Assistant Dean of Innovation, Strategic Initiatives & Distance Education & Prof. of Legal Writing at Suffolk University Law School

"No one has been focused on leveraging AI to improve the work of lawyers and accessibility to legal help longer than Tom Martin. His expertise is borne of the kind of in-the-trenches work that yields the highest value, and

Open Student Feedback

"Being your student has been an honor. I learned so much about how generative AI can impact legal services and cannot wait to implement what I learned in practice. Beyond the class materials, your class challenged me to explore the impossible – to seek efficient solutions using AI in ways that have not been considered before. The creative mindset that your class instilled in me will have a positive impact on the rest of my career."

— LT Jack Brandt, Suffolk Law; Operations Office, USCG

"I thoroughly enjoyed being a part of your class this semester.  As we discussed at length, generative AI is going to change the way we practice law, and I feel that I am at a huge advantage going into the job market next year having taken your course. Thank you for a wonderful semester, and I hope you have a great summer!"

— Nicole Harvey, Suffolk Law

"It was a great semester with you learning various AI tools and practicing with hands-on exercises! Thank you for guiding us through the material yo

Open Acknowledgements

Acknowledgements

First of all, I would like to thank Suffolk University Law School for giving me the opportunity to teach this course, Generative AI and the Delivery of Legal Services. I am forever grateful to Professor Dyane O'Leary for reaching out to me and generously sharing her class with me. Her work is inspiring, and I am honored to collaborate with her and the incredible faculty at Suffolk Law, including Dean Andrew Perlman, Assistant Dean Gabriel Teninbaum, and all my esteemed colleagues.

I would also like to express deep appreciation to my family for the countless hours, days, and nights they supported me while preparing this course and writing this textbook. Miriam, I couldn't have done this without your unwavering support. I am especially indebted to you for patiently listening to my endless discussions about AI; far more times than you probably ever wished.

My heartfelt gratitude goes out to all of my friends, colleagues, and fellow legal innovators in the legal tech community. Each a

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Open Preface

Preface

[for Law Professors and Law Librarians]

Generative artificial intelligence is already reshaping both the practice and the pedagogy of law. Generative AI and the Delivery of Legal Services aims to give students an intellectually rigorous yet approachable guide to this fast-moving landscape. No background in computer science, mathematics, or coding is assumed. Instead, each chapter blends plain-language narrative, real-world examples, practice tips, and “concept call-outs” that translate AI jargon into everyday legal reasoning. Whether their comfort zone is torts or tensors, every student can engage confidently with the material and leave the course ready to spot AI opportunities and pitfalls in practice.

A Living

Open The Parable of the Origami Crane

The Parable of the Origami Crane

Imagine spending years studying the art of origami under a master craftsman in a small, sunlit studio in Kyoto. The room smells of fresh paper and green tea, and dust motes dance in the beams of light that stream through rice paper windows. Your mentor, whose fingers have been creasing paper for seven decades, speaks little as he works. Instead, he communicates through the eloquence of demonstration: his weathered hands moving with the fluid certainty of water finding its path downhill.

Each fold, each crease, reveals something profound about balance and precision. The first few months, you simply observe and practice basic folds until your fingertips develop calluses and your mind begins to see the geometry hidden within the flat square. You learn that different papers have personalities, the handmade washi that resists then yields, the tissue-thin kami that responds to the slightes

Open Introduction

Introduction

When I was a young lawyer, the idea that a computer could help me to draft legal documents or predict case outcomes was a fleeting fantasy. We had Westlaw and Lexis for legal research and Shepherdizing cases, but the core of legal work (analysis, writing, strategy) rested squarely on our all too human shoulders.

Today, for you, the landscape is different and, I would argue, better. Generative AI tools can produce coherent drafts, suggest negotiation strategies, and even highlight risk factors buried in complex contracts. What does this mean for you, a law student preparing to enter a profession on the cusp of a technological transformation? It means your legal toolbox is expanding, and if you know how to use it wisely, generative AI can make you a more efficient, insightful, and future-now lawyer.

Who Is This Textbook For?

This book is for law students like you: people

Open Chapter 1: The Context of Generative AI

Chapter 1: The Context of Generative AI

Chapter Overview

Welcome to the beginning of our journey into Generative AI and the Delivery of Legal Services together. Here, we lay the groundwork for the rest of the course by providing an accessible yet comprehensive overview of what generative artificial intelligence (AI) is, why it matters, and how it came to be. We will begin by examining the preexisting world of programming based on conditional logic, highlighting its strengths but also its limitations, and progress to a historical view of AI’s evolution, including the different “waves” of AI innovation and “winters” of stalled progress. We will then zoom in on generative AI itself, explaining its fundamental mechanics and capabilities, while clarifying why this emerging form of AI represents a significant inflection point in both technology and society.

Toward the end of this chapter, we will look at the “ChatGPT Moment,” the catalytic event that brought generative AI to the attention of lawye

Open Chapter 2: How Does Generative AI Work?

Chapter 2: How Does Generative AI Work?

Chapter Overview

In Chapter 1, we discovered that generative AI is not just about classification or data analysis; rather, it creates original text and ideas based on patterns it has learned from existing information. We explored how these tools can streamline tasks like contract review and legal research, saving time and energy for legal professionals. We also discussed their limitations, such as the tendency to provide confident-sounding but erroneous answers, and the importance of using them responsibly.

In this chapter, we peel back the curtain on how these generative AI tools actually work. We will go step by step through the technical components, but in a way that remains accessible to a general audience (think: high school–level explanations). We will focus on the key ideas behind concepts like:

• Neural networks (the building blocks of modern AI systems)

• Large Language Models (LLMs) (like GPT-4o, o1, Claude, and others)

Open Chapter 3: Generative AI Models and Tools

Chapter 3: Generative AI Models and Tools

Chapter Overview

In Chapter 2, we dove into the inner workings of generative AI to see how neural networks, large language models, and the transformer architecture collaborate to produce human-like text. We discovered how attention mechanisms enable these models to handle context, and why data quality, along with thoughtful human oversight, matters so much. We also explored where generative AI excels, such as piecing together known patterns, and where it falls short, like truly novel scenarios. Ultimately, we learned that understanding the “nuts and bolts” of AI helps us gauge when and how to rely on these tools responsibly in legal practice.

In this chapter, we will focus more concretely on how to apply generative AI in the real world and what’s out there in the world of generative AI tools. You will:

• Compare proprietary large language models (LLMs) like ChatGPT and Claude with open-source models.

• Explore the distinctions betwe

Open Chapter 4: Legal AI Tools and Use Cases

Chapter 4: Legal AI Tools and Use Cases

Chapter Overview

In Chapter 3, we focused on the real-world landscape of generative AI in legal practice. We examined how to compare proprietary and open-source large language models (LLMs), the distinctions between predictive AI and reasoning AI, and how various popular tools, such as GPT-4, Google Gemini, and others, could fit into a law firm’s everyday workflow. By the end of that chapter, you understood why certain AI platforms are better suited for particular tasks, as well as how “copilot” models differ from “agent” models in the level of autonomy they bring to legal work.

In this chapter, we turn our attention to the legal-specific AI tools that can fundamentally reshape how lawyers and law firms approach tasks like contract review, document management, litigation strategy, and more. We will explore:

• What makes these specialized tools unique, including how they address confidentiality concerns and align with ethical obligations.
  
• **Key

Open Chapter 5: Bringing It All Together – A Review of Chapters 1-4

Chapter 5: Bringing It All Together – A Review of Chapters 1-4

Chapter Overview

This chapter serves as a thorough review of the key lessons and concepts we have covered in Chapters 1 through 4. Think of it as your one-stop refresher for everything from the historical context of AI to the cutting-edge tools shaping modern legal practice. By the end, you should have a cohesive understanding of how all these pieces fit together and how they guide your journey as both a law student and a future legal professional.

Purpose of the Chapter

In the first four chapters, we have built a foundational understanding of:

• What generative AI is and why it matters to the legal field.

• The evolution of AI, from simple, rule-based systems to powerful large language models.

• Technical underpinnings like neural networks and transformers, presented at a level accessible to a general audience.

• Practical applications of AI in the legal industry, ranging fro

Open Chapter 6: Prompt Engineering and RAG

Chapter 6: Prompt Engineering and RAG

Chapter Overview

In Chapters 1-5, we explored how Generative AI has begun reshaping the practice of law. We examined how it functions at a high level, surveyed popular models and specialized “legal AI” tools, and discussed the many ways in which it can assist lawyers, beyond just cranking out finished briefs or memos.

Now, we turn to one of the most practical facets of working with AI, Prompt Engineering, the art and science of writing effective instructions that guide AI models to produce useful, relevant, and accurate results. Mastering the fundamentals of prompt engineering can significantly elevate the quality of AI-generated work product, especially in the legal domain where precision and factual correctness are paramount. We will also explore Retrieval-Augmented Generation (RAG), a method of grounding AI responses in external data sources such as case law, statutes, and client documents. RAG is widely considered a key technique for enhancing A

Open Chapter 7: Impact of AI on the Business and Practice of Law

Chapter 7: Impact of AI on the Business and Practice of Law

Chapter Overview

In Chapter 6, we explored how prompt engineering empowers lawyers to guide AI models more effectively, ensuring that the output aligns with specific legal tasks and contexts. We contrasted naive prompts with more informed ones, examined the difference between single-shot and few-shot strategies, and introduced frameworks like RTF and RISEN to structure queries. We also learned how Retrieval-Augmented Generation (RAG) provides external grounding to reduce errors and keep responses up to date. By applying these techniques, legal professionals can craft more reliable, efficient AI interactions that enhance research, drafting, and client-facing services.

In this chapter, we'll learn how generative AI is transforming the legal profession, reshaping daily practice, business models, and professional roles. We will examine how AI streamlines tasks such as legal research, contract analysis, and litigation preparation, allowing l

Open Chapter 8: Ethical and Regulatory Implications of AI in Law

Chapter 8: Ethical and Regulatory Implications of AI in Law

Chapter Overview

In Chapter 7, we explored how generative AI is reshaping the legal profession by streamlining day-to-day work, adjusting law firm business models, and influencing professional roles. We discussed how automation can free attorneys from certain routine tasks, giving them more time for higher-level strategic thinking. We also examined how law firms are managing these changes, often by encouraging new skill development and adopting emerging technologies, while adhering to professional obligations.

In this chapter, we shift our focus to the ethical and regulatory frameworks governing lawyers’ use of generative AI in the United States. You will analyze how existing professional rules, such as those emphasizing competence, confidentiality, and candor, apply to AI-assisted legal practice. We will examine formal ethical opinions from the American Bar Association (ABA) and various state bars, newly minted court rules requiring

Open Chapter 9: Rethinking Access to Justice and Pro Bono

Chapter 9: Rethinking Access to Justice and Pro Bono

Chapter Overview

In Chapter 8, we explored how existing ethical and regulatory frameworks apply to lawyers’ use of generative AI, highlighting the duties of competence, confidentiality, candor, and supervision in this evolving landscape. We examined new court orders requiring AI disclosure, bar opinions emphasizing the need to verify AI-generated work, and real-world disciplinary cases that underscore the high stakes of failing to meet professional obligations. We also addressed the risk of bias in AI systems and how flawed training data or assumptions can lead to unjust outcomes. Overall, the chapter underscored that technology may change legal tools, but it does not alter a lawyer’s core responsibilities.

In this chapter, we examine one of the legal profession’s most important yet longstanding challenges: bridging the gap between people’s legal needs and the resources available to help them, often referred to as the “justice gap.” We explore

Open Chapter 10: Consolidated Review of Key Concepts from Chapters 6-9

Chapter 10: Seeing the Big Picture – A Review of Chapters 6–9

Chapter Overview

This chapter serves as a thorough review of the key concepts from Chapters 6–9, taking time to highlight the most important lessons, connect overarching themes, and offer reflection points to encourage deeper engagement with the material.

Purpose of This Chapter

Over the last few chapters (6–9), you’ve explored the nuts and bolts of using AI in the legal profession, with discussions on:

• Prompt Engineering and RAG (Chapter 6) – How to communicate effectively with AI models and use external data to make AI outputs more accurate.
  
• The Impact of AI on Law Practice (Chapter 7) – Ways that AI is reshaping the day-to-day roles of lawyers and the business models of law firms.
  
• Ethical and Regulatory Concerns (Chapter 8) – The rules, guidelines, and real-world examples that show how using AI carries serious responsibilities and requires careful oversight.
  
• **Access to Justice and Pro Bono (Ch

Open Chapter 11: Cultivating a Culture of Innovation and Continuous Learning

Chapter 11: Cultivating a Culture of Innovation and Continuous Learning

Chapter Overview

Welcome to Chapter 11, where we will explore how law firms, legal departments, and individual lawyers can foster a supportive environment for innovation and continuous learning. We will pay particular attention to the role of Generative AI and the organizational and cultural factors that encourage (or hinder) its successful adoption in the legal profession.

Simply acquiring an AI tool won’t transform a firm. Attorneys and staff must be willing and able to innovate. That means cultivating a mindset open to experimentation, refining processes through trial and error, and committing to ongoing professional development. Successful AI integration requires addressing psychological barriers, implementing structured change management, fostering collaboration across different roles, measuring real returns, and embracing continuous learning.

Upon successful completion of this chapter, students should be abl

Open Chapter 12: Transformative Change and the Future of AI in Law Practice

Chapter 12: Transformative Change and the Future of AI in Law Practice

Chapter Overview

Welcome to Chapter 12, our final chapter! We will examine how artificial intelligence is reshaping the practice of law, moving beyond simple automation to fundamentally changing how legal services are delivered. We will take a close look at online courts and virtual proceedings, explore how contracts are drafted and negotiated using AI, review new forms of AI-driven dispute resolution, and discuss the ethical, regulatory, and social questions that arise as our profession embraces these technologies.

Many legal visionaries, including Richard Susskind and Mark A. Cohen, predict that AI will lead to a redefinition of what it means to be a lawyer. I wholeheartedly agree. Already, we are seeing significant shifts, from the widespread adoption of ChatGPT and Claude, to experimentation with AI agents, and within legal, AI-powered legal research platforms, online courts, end-to-end automated contracts, and AI tools t

Open Workbook Introduction

Introduction to Workbook

Welcome to the workbook exercises!

The exercises below will help you put the ideas you have learned into practice. By completing them, you will begin to develop hands-on familiarity with using generative AI tools, such as ChatGPT, to explore legal concepts, test the tool’s capabilities, and experiment with prompt design.

The purpose of these exercises is not to test you on memorized facts, but rather to help you grow more confident and curious in your interactions with AI tools. You will learn to refine prompts, adjust tone and complexity, assess the accuracy of responses, and envision how AI might fit into real-world legal workflows.

Instructions for Completing the Exercises

1. Accessing a Generative AI Tool: Each exercise involves interacting with a tool like ChatGPT or a similar generative AI platform. Make sure you have reliable access to such a tool

Open Capstone Project: AI Integration Plan

Capstone Project: AI Integration Plan

Project Overview

For your final assignment in this course, you will work in small teams (3 students each) to develop a comprehensive AI integration strategy for a hypothetical law firm of your choosing. This project is your opportunity to synthesize the material from all chapters, ranging from technical fundamentals and ethical considerations to regulatory frameworks, cultural leadership, and future-oriented innovation.

By the end of the semester, you will present a cohesive plan that demonstrates both conceptual understanding and practical application of generative AI in legal services.

Project Objectives

1. Demonstrate Understanding of AI Tools:
   Show that you grasp the essential capabilities and limitations of generative AI in law (e.g., document review, legal research, client intake).
   
2. Apply Ethical and Regulatory Frameworks:
   Incorporate the rules of professional conduct, data privacy regulations, and considerations for

Open Chapter 1: Exercises

Chapter 1: Exercises

Below are five exercises designed to reinforce the core concepts covered in Chapter 1.

Each exercise is intended to be completed using the free version of ChatGPT (by OpenAI), available at https://chatgpt.com. The exercises build familiarity with prompt design, explore the capabilities and limitations of generative AI, and encourage reflective thinking about how this technology fits into the practice of law.

---

Exercise 1: Identifying the Evolutionary Stages of AI in Law

Purpose: To reinforce your understanding of how AI in legal services has progressed from rule-based systems to machine learning and then to generative AI.

Instructions:

1. Initial Prompt: Ask the AI: Summarize the three major waves of AI in legal services (rule-based systems, machine learning, and generative AI) and explain how they differ in functionality.
2. Refinement: If the first response is unclear, try refining your prompt to get a clearer explanation. For example: `Please

Open Chapter 2: Exercises

Chapter 2: Exercises

Below are five exercises designed to help you engage hands-on with these technical principles introduced in Chapter 2.

You will interact with a ChatGPT to see how changes in your prompts and context affect the output. By experimenting, you will gain firsthand insights into the model’s capabilities, limitations, and reasoning style.

---

Exercise 1: Explaining Neural Networks in Your Own Words

Purpose: To ensure you understand what neural networks are and how they learn patterns.

Instructions:

1. Initial Prompt to the AI:
   Explain what a neural network is and how it processes language, using a simple analogy suited for a first-year law student.
2. Refinement:
   If the explanation seems too technical, try:
   Please simplify the explanation further and use an example drawn from analyzing legal documents.

Reflection:
In your notes, summarize the AI’s explanation. Did the model’s analogy help you grasp the concept better? How do

Open Chapter 3: Exercises

Chapter 3: Exercises

Below are five exercises intended to deepen your understanding of the core concepts from Chapter 3.

Each exercise is intended for the free version of Claude (by Anthropic), available at https://claude.ai, taking advantage of Artifacts to produce tables and comparisons in a visual format. The instructions will prompt you to refine your input and evaluate the AI’s outputs critically.

---

Exercise 1: Comparing Predictive vs. Reasoning Models

Purpose: To demonstrate the differences between a predictive AI model (e.g., GPT-4o) and a reasoning AI model (e.g., o1 or o3) by generating and refining outputs for different legal tasks.

Instructions:

1. Initial Prompt to Claude
   Use a prompt asking Claude to produce a side-by-side comparison table (via Artifacts) of how a predictive model would handle drafting a simple legal disclaimer versus how a reasoning model would approach the same task. For example:
   `Create a table comparin

Open Chapter 4: Exercises

Chapter 4: Exercises

Below are five exercises designed to deepen your understanding of specialized legal AI tools discussed in Chapter 4.

Each exercise is intended for the free version of Claude, where you can use Artifacts (the platform’s feature for displaying tables and other structured data) to compare outputs, perform simple data analyses, and visually organize your findings.

---

Exercise 1: Comparing Legal AI Tools in a Table

Purpose: To explore and visually compare different AI products (CoCounsel, Spellbook, AI.Law, Alexi) based on key criteria from the chapter.

Instructions:

1. Initial Prompt to Claude:
   ``` Create a table comparing CoCounsel, Spellbook, AI.Law, and Alexi.com. Include columns for:
   - Data Security (features)
   - Domain-Specific Training
   - Ethical Compliance Features
   - Primary Use Case(s)
   - Unique Selling Point Provide a brief explanation in each cell, focusing on how each tool addresses these areas.

Open Chapter 5: Exercises

Chapter 5: Multiple-Choice Quiz

This is a review chapter and the in-class exercise for this chapter is an in-class, closed-book, multiple-choice quiz. Once the quiz is completed, the Professor will provide constructive comments to each student.

Open Chapter 6: Exercises

Chapter 6: Exercises

Below are five exercises designed to expand your mastery of prompt engineering concepts discussed in Chapter 6.

Each exercise is intended for the free version of Meta.ai, where you use structured approaches (e.g., RTF, RISEN) and explore the importance of clarity and context in your prompts (naive vs. informed prompts), and the difference between one-shot and few-shot strategies.

---

Exercise 1: Naive vs. Informed Prompts

Purpose: To see firsthand how adding detail and context (i.e., making your prompt “informed”) changes the AI’s output quality.

Instructions:

1. Choose a Simple Legal Task: For instance, “Draft a basic demand letter for a client owed $5,000 by a contractor.”
   
2. Create a Naive Prompt: For example:
   Write a demand letter to get money owed.
   Observe the AI’s response.
   
3. Create an Informed Prompt: Now add relevant details: ``` Draft a demand letter on behalf

Open Chapter 7: Exercises

Chapter 7: Exercises

Below are five exercises designed to help you explore the real-world impact of generative AI on the business and practice of law discussed in Chapter 7.

Each exercise is intended for Perplexity’s Deep Research feature (http://perplexity.ai/). Each exercise involves searching, reading, and synthesizing information in a dynamic, hands-on way.

---

Exercise 1: Surveying Real-World Case Studies

1. Search Prompt:
   “Law firms integrating generative AI for contract analysis”
   
2. Action:
   
   • Use Perplexity’s Deep Research to find two or three case studies of law firms that have implemented AI tools (e.g., Wilson Sonsini, Allen & Overy, etc.).
     
   • Summarize each case study’s key points: the business challenge, the AI solution, and the outcome.
     
3. Reflection:
   
   • In 2–3 sentences, note any common themes you see in how these firms balanced efficiency and ethical oversight.

---

Exercise 2: AI’s Impact on Junior vs. Senior Roles

1. **

Open Chapter 8: Exercises

Chapter 8: Exercises

Below are five exercised designed to illustrate how generative AI could raise various ethical dilemmas in different legal environments as discussed in Chapter 8.

How to Use These Exercises

Get Started:

1. Create a new notebook in NotebookLM. (Use https://notebooklm.google.com/).
2. Upload the PDF of Chapter 8 into NotebookLM. (This is to give the notebook of the chapter you are studying.)

Exercises:

1. Copy/Paste each scenario into your the NotebookLM's chat.
   
2. Copy/Paste the prompt under the scenario and review the AI’s response.
3. Review the AI’s ethical issue spotting and its analysis.
4. Reflect on any ethical issues the AI did not discuss, particularly around confidentiality, competence, bias, supervision, and candor. What did it get wrong? What did it get right? What do you think? Would you feel comfortable with this analysis? What else wou

Open Chapter 9: Exercises

Chapter 9: Exercises

Below are five exercises designed to teach you to engage hands-on with the principles introduced in Chapter 9 by using a no-code AI platform to create a knowledge assistant to provide legal information about housing issues using public information.

How to Use These Exercises

Get Started:

1. Create a free LawDroid Builder. (Visit https://lawdroid.com/subscriptions/lawdroid-open-access/ and create account). Make sure to use your suffolk.edu email address.

2. Await manual approval of your account. I will approve your account within 24 hours.

3. Approval of your account. Once you receive an email notifying you that your account is approved, you can log in here: https://lawdroid.com/.

4. Launch LawDroid Builder. Visit your my account page (https://lawdroid.com/account/) and use the "Launch" button to launch LawDroid Builder. It will take you to https://bot.lawdroid.com/bots.

5. Watch this Loom. Follow instructions in my [Loom video](https://www.

Open Chapter 10: Exercises

Chapter 10: Multiple-Choice Quiz

This is a review chapter and the in-class exercise for this chapter is an in-class, closed-book, multiple-choice quiz. Once the exam is completed, the Professor will provide constructive comments to each student.

Open Chapter 11: Exercises

Chapter 11: Exercises

For this exercise, you will apply what you have learned in this course to prepare your Capstone Project report and presentation.

Open Chapter 12: Exercises

Chapter 12: Exercises

For this exercise, you will apply what you have learned in this course to prepare your Capstone Project report and presentation.

Open Congratulations!

Congratulations!

You are now a GenAI Law graduate.

As you close this textbook and reflect on the journey we have taken together, I hope you see not just the complexity and potential pitfalls of generative AI in the law, but also the remarkable opportunities it can open up.

You have explored technical concepts, machine learning, natural language processing, large language models, learning what these technologies can do even without deep technical training. You have considered the ethical dimensions, from ensuring fairness and avoiding bias to maintaining client confidentiality and meeting your duty of technological competence. You have examined regulatory frameworks and understood how bar associations and governing bodies are grappling with the implications of AI. You have thought strategically about integrating AI into law firms, considered client relationships and communication, a

Open Key Words to Know

Key Words to Know

Imagine walking into a foreign country where everyone speaks a new language you've never encountered before. The streets are bustling with activity, signs hang everywhere, but the meaning escapes you. This is how many lawyers feel when first encountering the world of artificial intelligence. To navigate this new territory, we need to first understand its language.

Let's begin our journey by exploring the fundamental terms that will serve as our compass in this brave new world of legal technology. Like learning any new language, we'll start with the basic building blocks and gradually construct a deeper understanding.

The Foundation: Large Language Models (LLMs)

At the heart of modern AI lies what we call Large Language Models, or LLMs. Think of them as vast libraries that have consumed billions of books, articles, and conversations, distilling all this knowledge into patterns and connections. But unlike traditional libraries where you need to know exactly where to look, these di

Open Glossary

Glossary

Below are concise definitions of important terms, arranged alphabetically by chapter.

Chapter 1

AI Winters
Periods in the history of artificial intelligence when excitement and funding dried up due to unmet expectations, causing research progress to slow or stall.

AlphaGo
An AI program created by DeepMind that mastered the complex board game Go. Its victory over world champion Lee Sedol in 2016 demonstrated the power of combining deep learning with reinforcement learning.

AlphaZero
A successor to AlphaGo, also developed by DeepMind. Unlike AlphaGo, AlphaZero was not only able to learn Go from scratch but also mastered Chess and Shogi—all using self-play without domain-specific programming.

Artificial General Intelligence (AGI)
A theoretical form of AI capable of understanding or learning any intellectual task that a human being can, rather than being limited to one domain (e.g., image recognition or language translation).

**Artificial Intelligence (AI

Open Minilectures

Minilectures

Chapter 1: The Context of Generative AI

Chapter 2: How Does Generative AI Work?

Chapter 3: Generative AI Models and Tools

<iframe width="560" height="315" src="https://www.youtube.com/embed/hcOIS2Rq16Y?si=3km8LfNdweZZFXok" title="YouT

Open Podcasts

Podcasts

Chapter 1: The Context of Generative AI

Chapter 2: How Does Generative AI Work?

Chapter 3: Generative AI Models and Tools

<iframe width="315" height="315" src="https://www.youtube.com/embed/5ooX1pzn0PM?si=Uts

Open Chatbot Tutor

Chatbot Tutor

Open About the Author

About the Author

Thomas G. Martin is a Generative AI author, professor, philosopher, coder, lawyer, and sought-after speaker dedicated to transforming the legal industry. As the CEO and founder of LawDroid, a pioneering Generative AI Legal Technology company, and co-founder of the American Legal Technology Awards, Tom stands at the forefront of legal innovation.

Recognized as an ABA Legal Rebel and Fastcase 50 Honoree, Tom is a thought leader in the legal technology space. He shares his expertise as an Adjunct Professor at Suffolk University Law School in Boston, where he teaches Generative AI and the Delivery of Legal Services.

Tom’s captivating presentations have inspired audiences at major events, including ABA TechShow, LegalWeek, ILTACON, Clio Con, and Legal Innovators. His insightful writing has been featured in publications such as the *National Law Rev

Open Changelog

Changelog

As artificial intelligence, and its adoption within the legal industry, is rapidly evolving, I will be making changes to this textbook from time to time. This changelog will indicate the nature of the update and the date on which it was made. The version numbers are arbitrary and do not signify the extent of the changes.

0.0 - This is the initial version of this text that I used to teach LAW 4009 2 Generative AI and the Delivery of Legal Services at Suffolk University Law School.

0.1 - April 22, Added Endorsements page.

0.2 - May 6, Added Subscribe page.

0.3 - May 12, Added Acknowledgments page.

0.4 - May 14, The Parable of the Origami Crane added.

0.5 - May 17, Preface for law professors and law librarians and welcome video added.

0.6 - May 25, Added Student Feedback page.