concept for the legal profession. It is already in use across law firms of every size, and attorneys who do not understand it are increasingly at a disadvantage. AI for Lawyers: Power, Pitfalls and Prompting, presented by Jeffrey Allen of Graves & Allen and Ashley Hallene of Demeter Land Development, LLC, is a practical, beginner-friendly course that walks attorneys through what AI actually is, how it can support legal work, where it can go wrong, and how to use it responsibly under the rules of professional conduct.
Before attorneys can make sound decisions about AI, they need to understand the technology in plain terms. The course opens with a clear explanation of what artificial intelligence is and how modern generative AI tools function, without requiring any technical background.
Generative AI tools produce content in response to user input. They are trained on large volumes of text and learn patterns in language that allow them to generate responses, drafts, summaries, and analyses. Understanding this basic mechanism matters for lawyers because it clarifies both what these tools can do and why they sometimes produce results that are convincingly wrong.
This foundational section gives attorneys a working mental model of AI before the course moves into practical application.
The course moves quickly from theory to practice, identifying concrete ways AI tools can assist attorneys in their day-to-day work. These include:
Drafting assistance for correspondence, contracts, motions, and other documents that require a strong starting point but benefit from attorney review and judgment;
Research support to help identify relevant legal concepts, generate initial issue lists, and surface background information on unfamiliar areas of law;
The course is careful to frame these as tools for assistance, not replacement. The attorney remains responsible for the accuracy, completeness, and professional quality of every work product, regardless of how it was generated.
Many attorneys already use practice management or case management platforms and wonder how automation fits into that picture. Marsaw addresses this directly: automation and case management systems serve different functions, and many firms will benefit from both.
The two categories can and often do work together. A case management system might house the client data, while an automation layer handles the communications and task assignments that flow from it. Treating them as interchangeable leads firms to either over-rely on manual data entry or underuse the time-saving potential of trigger-based workflows.
This is where the course adds the most value for attorneys who already have some familiarity with AI. Using these tools is not ethically neutral, and the course works through the specific professional responsibility obligations that apply.
Competence: Model Rules require attorneys to keep pace with changes in the law and its practice, including the benefits and risks of relevant technology. Attorneys who use AI tools without understanding how they work or where they fail may struggle to meet this standard.
Confidentiality: AI tools that process client information raise serious confidentiality concerns. Attorneys must understand how any platform they use handles data, where it is stored, whether it is used to train models, and what protections are in place. Using a general-purpose AI tool to process sensitive client details without vetting the platform first creates real exposure.
Supervision: Just as attorneys are responsible for the work of non-lawyers who assist them, they are responsible for the outputs of AI tools they put into use. Supervision obligations do not disappear because a task is handled by software. Attorneys must review AI-generated work and cannot rely on it without independent verification.
Candor to the Tribunal: Submitting AI-generated legal research or citations without verification can lead to the submission of fabricated authorities. Courts have sanctioned attorneys for exactly this failure, and the course addresses the obligation to verify before relying on any AI-generated legal authority.
Rather than presenting abstract frameworks, this course walks through real-world examples of how automation transforms everyday firm operations.
A typical intake workflow, for example, might work as follows:
A prospective client completes an online intake form;
The system automatically sends a confirmation email and schedules a consultation;
If the prospective client does not respond within 48 hours, a follow-up message is sent automatically;
Upon confirmation, the system generates a retainer agreement pre-populated with the client's information;
Once the agreement is signed, a matter is created in the firm's case management; system and tasks are assigned to the appropriate team members.
What might have taken two or three hours of staff time per new client, including phone calls, manual data entry, and document drafting, now runs with minimal intervention. The attorney's attention is reserved for the consultation itself and the legal work that follows.
Similar efficiencies apply to deadline management, billing reminders, and routine case status communications.
The course gives significant attention to the practical risks that arise in real-world AI use, including risks that have already caused problems for attorneys in practice.
Inaccurate outputs are a persistent issue. AI tools generate fluent, confident-sounding text even when the underlying content is wrong. Legal reasoning that looks thorough may contain errors in fact, logic, or application that only careful review will catch.
Hallucinated citations are among the most well-documented AI failures in legal settings. Generative AI tools have produced citations to cases that do not exist, with plausible-sounding names, dockets, and holdings. Attorneys who submit these citations without verification face professional and court-imposed consequences.
Confidentiality exposure arises when attorneys input client information into AI tools without understanding how that data is handled. The course addresses practical steps for evaluating platforms and protecting client information when using AI tools.
The final section of the course is devoted to prompting, the skill of giving AI tools instructions that produce reliable and useful results. This is a practical, learnable skill, and the course treats it as such.
Effective prompting involves giving the AI enough context to understand the task, specifying the format and level of detail needed, and identifying the limitations or constraints that apply. An attorney who prompts an AI tool the way they might ask a research assistant for help will generally get better results than one who submits bare questions and accepts the first output.
The course introduces prompting techniques that attorneys can apply immediately, with examples drawn from common legal tasks. It also addresses how to evaluate AI output critically, recognizing when a response is plausible but not verified, and knowing when additional review is required before relying on anything the tool produces.
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