How to Use AI in Private Practice

Being the tech-forward lawyer that I am, of course, always exploring what AI can do. BUT I also use AI with my eyes wide-open because I don't want to redo work and don't want to rely on inaccurate improper content. Here are a few specific use cases that have worked for me and a checklist for getting started responsibly: 1. Drafting Routine Agreements Faster Take a simple NDA or basic MSA. I have a template, but often need to make adjustments based on negotiations or other factors. I use Malbek's AI to generate a new draft by giving it given explicit guidelines. Because the AI integrates with Word, I can make edits directly and align the language with our internal templates. It’s not a final product—but it’s a faster starting point, and it cuts down on manual work without compromising review quality. 2. Creating Plain-Language Legal Summaries When I need to explain a complex regulatory update or court decision to our product team, I’ll often use AI to produce a first-draft summary. Again, I never rely on this output blindly—but it speeds up the initial framing. I rewrite it with the business audience in mind, but I’m no longer stuck staring at a blank screen. 3. Reviewing Vendor Contracts for Key Terms When we onboard new vendors, I often review lengthy agreements under tight deadlines. I’ve used AI to extract core clauses—termination rights, notice periods, auto-renewals—into a structured summary. This lets me validate critical terms quickly and spot potential issues without hunting through 20 pages of dense legalese. It doesn’t eliminate the need for review, but it helps me prioritize my time and focus. What I don’t do (yet): -I don’t let AI write legal arguments or regulatory guidance. -I don’t input confidential or proprietary data unless it’s inside an enterprise or securely managed tool. My Checklist: -Choose legal-trained AI tools like one within a CLM like Malbek's. -Use secure, vetted platforms—or redact sensitive data. -Treat AI like a junior analyst: helpful, but not infallible -Always validate legal conclusions before internal or external distribution The bottom line: AI isn’t replacing legal judgment—but it can extend our reach. If it helps me deliver work faster without cutting corners, that’s a meaningful win. If you're experimenting with AI in your legal function, I'd like to know: Where have you seen real value? Where have the risks outweighed the benefits? #legaltech #innovation #law #business #learning

How I Use AI as In-House Counsel Are you one of those attorneys who uses checklists to review an agreement? First, good for you. You're so organized. Second, checklists are perfect for AI! Many attorneys don’t trust AI to review an entire contract yet. But even the most skeptical lawyer can save time by using AI for the more administrative parts of a review. Here’s a real prompt I use with my enterprise-grade Legal AI tool to do a preliminary review of an incoming agreement against my checklist. Step-by-step 1. Upload the contract and your checklist. If you’re using a public model, you might consider prepping the document and/or your system settings. 2. Prompt “# Instructions 1. Perform a comprehensive review of the attached contract using the checklist as a guide. The review should be from the perspective of [party name]. 2. Create a detailed analysis table with these columns: - Checklist Item: Summarize the requirement in one sentence - Contract Language: Exact quotes from the contract - Section: Where the language appears - Analysis: Whether the language satisfies the checklist item - Risk Level: 🔴 High / 🟡 Medium / 🟢 Low or None - Recommendation: How to fix or improve the clause [3. For each checklist item:    a. Identify relevant sections    b. Extract exact quotes with citations    c. Evaluate adequacy    d. Assess risk to [party designation]    e. Recommend improvements] 4. After the table, provide: a. Executive summary of top issues b. Prioritized list of recommended changes c. Any risks specific to [industry] business" 3. Review & Edit Nothing replaces your legal brain. I double-check the analysis, then use the table as a guide for redlining. 💡 Notes 1. Setting up a table in a prompt is a little more involved. Save the prompt so you can reuse the table structure next time. 2. You may not need [Number 3]. If you’re not getting a clear enough response, add in #3 which will give the model more specific instructions. ⚠️ Public Models This one is trickier to use with a public model like ChatGPT, or Claude since you're uploading an agreement. Consider doing the following: 1. Turn off training. For ChatGPT, consider using a temporary chat. 2. Thoroughly redact names and sensitive info and replace them with generic terms. 3. Persona Prompt at the start: “You are an experienced in-house counsel who is an expert contract reviewer.” 4. Consider using a generic account where you haven't already indicated where you work.  5. Use your own judgement as an attorney. Unfortunately, you might not be able to upload an entire contract into a public AI model and protect your data and confidentiality. It might depend on the type of agreement or how well you can redact it. Ultimately, you need to use your judgement as an attorney to determine how extensively you can use AI for this use case. Want help building your own prompt or refining your workflow? Drop a comment.👇 #LegalAI #InHouseCounsel #ContractReview #LegalTech #AI

The AI world just got a serious upgrade, and lawyers should take notice. OpenAI’s new Deep Research tool isn’t just another chatbot that spits out text based on what it was trained on months (or years) ago. It’s an AI agent—one that can go out, dig through the internet in real-time, and pull together well-cited, structured research reports. For lawyers, that’s a big deal. Research is the backbone of good lawyering, but it’s also a huge time sink. If an AI can handle the grunt work, that’s a potential game-changer. But how does Deep Research actually work? Let’s break it down. If you’ve used ChatGPT or other GenAI tools before, you know the drill: you type a question, the AI processes it, and—voila—it spits out an answer. But there’s always been a problem. Traditional AI models, no matter how advanced, are only as good as the data they were trained on. That means if the model’s training cutoff was months ago, it no longer has access to newer information. And even if it could pull real-time data, AI has always struggled with multi-step reasoning—breaking down a complex research task into smaller pieces, hunting down sources, and piecing everything together. Deep Research changes that. Instead of just generating answers based on its training data, it thinks through the problem like an autonomous agent. It creates a research plan, executes multiple steps, backtracks when necessary, and refines its findings. Then, it delivers a structured report, complete with citations, all in about 5–30 minutes. So, is this the end of traditional legal research? Not quite. You still need to verify everything. AI can make mistakes, misinterpret laws, and overlook nuances that a trained legal mind would catch. But as a research assistant, Deep Research is poised to make AI an even more indispensable tool. - I’m Joe Regalia, a law professor and legal writing trainer. Follow me and tap the 🔔 so you won't miss any posts.

On a day dominated by DeepSeek AI, LexisNexis dropped a significant AI addon called "Protege." Some excellent details via ABA Journal and Danielle Braff in the comments: 1️⃣ "The new program uses Lexis+ AI technology to complete tasks based on the user’s goals. This includes its ability to draft transactional documents, litigation motions, briefs and complaints. A key component of Protégé is its ability to check its work, the company said in a press release." 2️⃣ "Users can upload tens of thousands of legal documents to the Protégé vault, and the technology can summarize, draft and research the documents. Protégé can also create a timeline of events from that paperwork. It can help users discover similar motions and arguments to refine their strategy and identify weaknesses. Users can ask Protégé to summarize complex documents up to 300 pages long, which is a 250% increase over previous limits, the company said." One of the primary reasons AI has been slowly adopted in law firms is the very real risk of hallucincations or fake resources used to create motions. This has already been penalized in multiple litigation cases with multiple attorneys. The difference is most of those attorneys used public large language models that can be free or light cost. This version of Agentic AI has aspects that can be selectively trained on only the documents/data provided while utilizing pre-designed rules and time saving methods. I'm excited to work with LN to discuss the security specifications and help to promote data privacy and ethical use of AI for Law Firms that are open to innovation. If you are an attorney, does this move the need for you?

💡 Last week at ABA TECHSHOW, Greg Siskind and I presented “60 AI Use Cases in 60 Minutes.” Mark C. Palmer was graciously “voluntold” from the audience to be our timekeeper—because nothing says fun like trying to wrangle two lawyers with microphones and a countdown clock. (See attached pic of him in action, front row center, radiating equal parts focus, regret, and “how did I get roped into this?”) Not all heroes wear capes! 🦸 Greg and I covered how lawyers can use generative AI, 🤖 both legal-specific and consumer tools like ChatGPT and Gemini, across different parts of their practice—from client intake to drafting to internal operations. These weren’t future predictions—they were real, specific use cases that firms can test now. ✅ Over the next six posts, I'll share my 30 tips. Each post will include 5 practical tips or use case ideas to explore. Here’s Part 1 ⬇️ 🔷 Assist with GenAI prompts Use case: Generate prompts that will provide the best response possible. Prompt: “Provide a prompt that will provide the necessary output that will assist me in accomplishing X.” 🔷 Voir dire questions Use case: Generate targeted questions to assess potential jurors’ suitability for a trial. Prompt: “Create voir dire questions for a civil case involving personal injury to evaluate jurors’ biases and potential conflicts of interest related to X issue.” 🔷 Cross-examination questions Use case: Develop strategic questions to challenge a witness during cross-examination. Prompt: “Generate a list of cross-examination questions for a witness testifying about defense of a third-party self-defense claim in a murder case.” 🔷 Drafting deposition questions Use case: Generate specific, targeted questions for depositions, most likely with a legal-specific tool. Prompt: “Generate a list of deposition questions for a defendant in a medical malpractice case focusing on their handling of patient care.” 🔷 Deposition summary Use case: Condense a deposition transcript into key points, facts, and relevant testimony, most likely with a legal-specific tool. Prompt: “Summarize the main points from this deposition transcript of witness X, highlighting key testimony, admissions, and contradictions.” information for a personal injury case intake.” 💬 Have you tried one of these? Do you have your own example? Drop it in the comments.👇 📌 Follow to catch the rest of the series over the next few weeks. #legaltech #AI #ABATECHSHOW #ABATECHSHOW25 #nbroundups