AI FOR LAW FIRMS

Build An AI-Powered Practice — Without Risking Compliance or your Ethics

Training, consulting, and tools for lawyers who want to use AI safely, profitably, and on their own terms. No hype, no fluff, no jargon.

See Engage

Explore Courses

Sign Up For Consulting

Featured article

‘Reports of My Death Have Been Greatly Exaggerated’ — Prompt Engineering

Amy Swaner

Aug 2026

Calculating...

Prompt engineering has been declared dead so many times that the obituaries now have their own genre. IEEE Spectrum ran the headline "AI Prompt Engineering Is Dead" as early as March 2024 — though they hedged at the end to say "long live AI prompt engineering." I wasn't convinced. That October, I published a full Advanced Prompt Engineering guide — because the skill was very much alive and, for lawyers, worth doing well.

Maybe just the title died. Job postings with "Prompt Engineer" in the title have fallen sharply since late 2024, and in Microsoft's survey of 31,000 workers across 31 countries, "prompt engineer" ranked second-to-last among roles companies plan to add. But the ability still matters, it’s just now assumed. As Nationwide's CTO put it bluntly, prompting is becoming "a capability within a job title, not a job title to itself." Makes sense.

While postings titled "Prompt Engineer" declined, LinkedIn postings that list prompt engineering as a skill grew roughly 250%. Compensation for work that leans on the skill has risen, not cratered. That doesn’t scream rigor mortis to me.  Prompt engineering is following the path of "web design" or "spreadsheet skills." Nobody advertises "HTML expert" anymore, but the underlying literacy is assumed. — invisible because it's everywhere, not because it's gone.

So despite the LinkedIn posts to the contrary, prompt engineering hasn’t died. I’d say it’s grown up a bit. No more of the cheap flattery a la “You’re the best ever legal expert” and cheap tricks such as “getting the correct answer is a matter of life and death.” If you’re honest, I’d bet you used some of those tricky threats and promises but you no longer do.

What Prompt Engineering Used to Look Like

The models got good enough that "tricky" prompting stopped mattering. The were valuable prompting techniques that I taught and used. In fact, I still use some of them. And then, there were some outlandish 'techniques' that were actually only puffery. Things like bribes ("I'll give you a $1MM"), saying someone's life depended on the correct answer, or spending more time framing the role than the actual substance in the ask were hokey time wasters.

The Techniques That Died Before the list of what works, here's what to stop doing — the prompting rituals that smarter models now make pointless:

  • Flattery. "You are a world-renowned expert with an IQ of 200." The model is not motivated by praise. A real role and a quality standard (technique 1, below) still help; the puffery doesn't.

  • "Take a deep breath" / "I'll tip you $200." These genuinely circulated as performance hacks. They were always throwaways--based more in hope and superstition than good practice. Research has shown them to be ineffective when tested systematically.

  • Chain-of-thought on everything. Forcing "think step by step" onto simple prompts just pads the output unnecessarily. And current reasoning models already show their work when it matters.

  • Elaborate few-shot examples for routine tasks. Few shot prompting had a real and justifiable basis. It was even useful to obtain things as basic as formatting. It's now overkill for anything but very specific needs.

The machine can now prompt itself. Research has found that prompts auto-generated by the model frequently outperformed the ones humans hand-crafted. Tools that optimize prompts for you are now routine, and Gartner has projected that in 2026 a large majority of enterprises will lean on AI-driven prompt automation. If the model writes a better prompt than you do, you might as well use it.

Prompt Engineering’s Glow Up

Does anyone still say that – “glow up”? Maybe I’m once again revealing my age. But prompt engineering still matters. Especially with regard to instructions to agents. It just looks more sophisticated and elegant now.

Its gone from tricks to techniques. You still need to state exactly what you want, with the right context, constraints, and success criteria — which is just thinking clearly about the problem. No model eliminates that, and if we hand models bigger and riskier jobs, our task goes from coaxing a mediocre model into giving us something we can work with, to precisely directing a powerful one. You know this instinct from any professional context: a well-framed research question beats a vague one every time. and the model will not fix the imprecision you left in.

A noticeable shift. Although most AI models now really help with prompt engineering, we still nee context engineering — designing everything the model sees on a given call, not just the sentence you type. When Andrej Karpathy and Shopify's Tobi Lütke both publicly argued in early 2025 that context engineering was the real skill worth developing, the industry picked it up fast. (I wrote about why this matters for lawyers deploying agentic systems in my piece on the Clawdbot disaster — when your AI has broad authority and no judgment, what it can see is a governance problem, not just a quality one).

Seven Ways to Improve What You Get from your AI Tool

These are the techniques I actually use, and recommend to lawyers, in legal work and consulting. Every one is a way of removing ambiguity the model would otherwise resolve by guessing. Remember what these tools are: prediction machines trained to produce the response a human is most likely to approve of. Reinforcement learning from human feedback improves how useful they seem, but it can also teach them to tell you what you want to hear over what's true — a documented failure mode commonly called sycophancy.


Article content

1. Assign a role and a standard that will help shape your output. "You are a helpful expert" does nothing. "Respond as a litigator reviewing this for a motion to dismiss; flag anything a judge would question" gives the model a focus and a quality bar. The role narrows the acceptable answers. When you need a specific voice or format — your own personal writing style, a particular precedent — show it an example rather than describing it ("match the structure and register of this"). That's the only time I still bother; for ordinary asks the models no longer need the hand-holding.

2. Specify format when the format affects the substance. For a one-off, you can get the content first and reformat later, no worries. But here are two situations when you need to state the format up front. First: when you'll reuse the prompt, and second, when the format changes how the model thinks, not just how it looks.

For example, ask a model to extract contract terms into a table with columns Provision | Party Obligated | Trigger | Deadline | Source §, and your AI reads the document obligation-by-obligation to fill each cell — it can't leave a column blank without exposing the gap. Whereas if you ask it to “summarize the key terms" you get a smooth paragraph or bullet points that might just skip that one obligation on page 14 that didn’t seem as important. See how the format changed the output? Same document, different reasoning — because the format forced completeness. Consider whether your prompt should include a format to help force completeness.

3. Give context and constraints, not just the task. The task is "draft a client update." The context is who the client is, what they already know, what tone fits, and what you must not say. Any ambiguity you leave in, the model will fill in for you — usually in a glossy way that isn’t especially noticeable, and worse, sometimes with content that is wrong but is hard to catch because it looks good. This is an incredibly important principle.

4. Set the boundaries of what you don't know — and make it permanent. Tell the model to flag uncertainty, separate what it's confident about from what it's inferring, and say when something can't be verified. To make this easier on yourself, stop retyping this every session and put it into persistent settings at two levels. The first is an account-wide layer; your global/custom instructions (the "personalization" settings most tools now offer). Mine include this imperative: never invent a citation; if you're not certain a source is real, say so, and flag any assumption you're making. If you state a fact, give me a live link to a primary source. I say some variation of that three different times in my Claude global instructions.


Article content

Use global or system instructions for your AI Tool -- here, Instructions for Claude

You can also add it to a matter-specific layer is a Project, Custom GPT, Gem, or folder — a scoped workspace where you load the relevant documents and standing instructions for one client or workstream, so the model carries that context into every conversation without you re-explaining. Think of it as one custom instruction set for how you always work, and one per matter for what this work is.

Article content

Attach instructions to a custom GPT (eg. Gems in Gemini, GPT's in ChatGPT, Projects in Claude)

Article content

Attach instructions to a folder

5. Don’t believe everything your AI tells you. There's a running debate that AI is making us lazier thinkers. I'd argue the opposite: you have to think harder now, because the answers come out silky smooth like a buttery chocolate truffle — easy to let them melt away, lulling you into a brief happy stupor, whether or not they're right. The skill you need to develop is resisting that gloss long enough to check that the substance is correct. It sounds good even if it’s wrong, and you would have caught it had it not been stated so smoothly. Think through all of the principles. Or better yet, add all of your points, or contentions into your AI tool, then ask the AI to build around them. Follow up by asking what you missed, or three ways to make to make the output better. But always, always, challenge the reasoning, demand the source, or ask "what would make this incorrect?" If in doubt, push back and make it defend its answer.

6. Engineer the context. For anything beyond a one-off, control the whole information environment — which documents the model can see, what's in the system instructions, what history it's carrying, what tools it can call. A mediocre prompt with the right context beats a beautiful prompt with the wrong context every time. Models have real limits on what they can reliably hold and reason over at once. Even the newest, most powerful iterations of AI tools how context limits. Use those context windows to your advantage. For example don't ask for ‘a full contract analysis’ of a 250 page complex contract in one shot. Ask it to (a) extract every obligation, then (b) flag the unusual ones, then (c) draft proposed edits. Each step stays within the model's reliable working range, each is checkable, and errors don't compound without you knowing — which, not coincidentally, is also how you keep a human in control of the output. Gain insight into and understanding of context and how to use context engineering to your benefit here.

7. Have your model interview you before it answers. This is the most underused technique I know, and it genuinely improves your output.  It works precisely because the models got better at recognizing what they don't know. Instead of trying to anticipate every constraint yourself, make your AI tool do the work. I end nontrivial prompts with "What questions do you have for me before you start?" You'll get back questions that will help to fill in the gaps you didn't think to fill. Even with AI tools that are fairly good at asking follow up questions without being prompted, I still find this gives me the best output.

The Bottom Line

Prompt engineering was a thing. It was important and necessary. Not so much anymore, but there are still techniques that make your output higher quality. And two more quick bonus tips: First, your AI tool might have the “perfect my prompt” button. Sure, it will fill out a thin prompt, but read this ‘plumped up prompt’ carefully. Is it really better, or it is making assumptions that are generally appropriate, but are skewing the answer? Second, please ALWAYS CHECK EVERY CITATION. Always.

© 2026 Amy Swaner. All Rights Reserved.  May use with attribution and link to article.

Prompt engineering isn’t dead—it has grown up, replacing tricks and flattery with a more thoughtful approach that helps lawyers give AI better context, set clearer boundaries, and produce more reliable results.

Read

AI Resource

Quick Reference: Risk Mitigation Checklist for AI Notetaker & Transcription Bots

AI in Legal Practice

Jan 2026

Use this one page checklist to minimize risks for you and your clients when considering AI Notetaker and Transcription Bots.

This easy reference covers bots in meetings, and vendor selection.

Share directly with your clients.

Download
Featured article

The Cover-Up Is ALWAYS Worse Than the Hallucination

Amy Swaner

Aug 2026

Calculating...

TL;DR

Two recent federal court orders — Miller v. Regions Bank (N.D. Ala. May 21, 2026) and Couvrette v. Wisnovsky (D. Or. Dec. 12, 2025, with follow-on order Mar. 23, 2026) — show that the worst-case outcome of AI use is rarely the hallucination itself. It is the lawyer's response after the hallucination is caught. Both courts say plainly to the attorney-offenders 'own the mistake or lose your case, your bar standing, or both.' This article walks through the two orders, then gives firms 7 Best Practices to put in place in your firm.

The Cover-Up -- Miller v. Regions Bank

On November 26, 2025, a solo practitioner in Alabama named H. Gregory Harp signed up for a paid ChatGPT Plus subscription. Four days later, he filed an opposition to summary judgment in Miller v. Regions Bank, No. 2:24-cv-1324-HDM (N.D. Ala.). The brief contained four quotations attributed to real Eleventh Circuit and D.C. Circuit decisions. The cases were real. The quotations were not.

U.S. District Judge Harold D. Mooty III pulled the proverbial thread. With falsity now exposed, he issued a show-cause order on the four false quotations and ordered Harp to either produce the underlying authorities with the quotations highlighted, or submit a sworn declaration explaining how the brief was generated.

Harp's first response he had not used AI.

At the April 20, 2026 show-cause hearing, that story changed a bit. Harp now said he used ChatGPT "to do a general search," but no AI "wrote a single word" of the brief. Pressed, he conceded the quotations "could have been generated by ChatGPT" — but he had not checked his ChatGPT history. He volunteered, on the record, to pull the log.

The court took him up on it. The same day, Judge Mooty entered an in camera production order to turn over the screenshots of every ChatGPT conversation related to the case.

Three days later, on April 23, Harp deleted his ChatGPT account. Not just canceled it — he requested a prorated refund, which forfeited his read-only access through the end of the billing cycle on April 30 (one day past the court's production deadline). His later declaration framed the deletion as a contrite, post-hearing decision: "After the hearing I was extremely upset that I had used ChatGPT at all." The OpenAI emails he was forced to produce told a different story.

The Court's view of that framing is worth quoting:

"The court is not ordering the harshest of Attorney Harp's sanctions because he made a mistake. The court is ordering them because, when confronted with that mistake, he chose dishonesty over candor and destruction over disclosure. Lawyers make errors. Competent and ethical lawyers own [their mistakes]."

On May 21, 2026, the court sanctioned Harp under Rule 11, its inherent authority, and Alabama Rule of Professional Conduct 3.3, and held him in civil contempt. The order:

  1. Publicly reprimands him, with a publication requirement (he must serve the order on every client, opposing counsel, and judge in his open cases);

  2. Disqualifies him from Miller and stays the case for 60 days to let the client find new counsel;

  3. Suspends him from practice in the Northern District of Alabama for six months;

  4. Refers the matter to the Alabama State Bar; and

  5. Draws an adverse inference that ChatGPT generated the fabricated quotations (and likely other misstatements the court flagged), based on the bad-faith spoliation finding.

Critically--and this is the heart of the point I’m trying to make--the Court drew a sharp line between the underlying error and the cover-up. The Rule 11 sanction for the false citations was the public reprimand. The disqualification, suspension, and bar referral were for the cover-up; the lies and the spoliation.

The Skyrocketing Dollar Figure -- Couvrette v. Wisnovsky

If Miller is the cover-up case, Couvrette v. Wisnovsky, No. 1:21-cv-00157-CL (D. Or.), is the volume case — and, as of this writing, the largest AI hallucination penalty in U.S. history.

The underlying lawsuit was a family fight over Valley View Winery in Jacksonville, Oregon. Joanne Couvrette sued her two brothers for control of the family vineyard, alleging elder abuse and seeking roughly $12 million. In early 2024, San Diego attorney Stephen Brigandi appeared pro hac vice for Couvrette. Because he was not licensed in Oregon, Portland landlord-tenant attorney Tim Murphy signed on as local counsel.

Across three briefs related to cross-motions for summary judgment, the plaintiff's filings contained 15 nonexistent cases and ten fabricated quotations. When defense counsel flagged the problems, Brigandi did not concede. Instead, he filed a useless Notice of Errata, without acknowledging the fake cites or quotes. And here’s the funniest part to me — he re-filed without verifying the remaining citations, which themselves continued to misstate the law.

Judge Clarke's opinion is unsparing, showing he had no qualms in terminating a $12.6 million claim:

"In the quickly expanding universe of cases involving sanctions for the misuse of artificial intelligence, this case is a notorious outlier in both degree and volume. ... If there was ever an 'appropriate case' to grant terminating sanctions for the misuse of artificial intelligence, this is it."

The cover-up won the attorneys and their client a sanctions package of approximatey $110,000. Judge Clarke's December 12, 2025 Order, his Order from March 23, 2026 and his Order from March 30, 2026 set forth the sanctions and award of attorneys' fees. It's structured to hit three different people:

Brigandi (pro hac vice counsel)

$80,498.72 in opposing-party attorney fees plus $15,500 in fines. The court also struck the sanctionable briefs without leave to refile.

Murphy (local counsel):

$14,205.66 — 15% of defendants' fees — imposed in the follow-up order of March 23, 2026 for failure to meaningfully supervise pro hac vice counsel.

Couvrette (the client):

The court found she shared responsibility and so her claims of $12.6 million were dismissed with prejudice as terminating sanctions, and the matter was referred to the Oregon State Bar.

The lesson from Judge Clarke matches Judge Mooty's, even though the misconduct profile is different. Clarke wrote, "Plaintiffs and their counsel have not been adequately forthcoming, candid or apologetic about their conduct." And on the attempted fix, "There is no evidence that Mr. Brigandi took any steps to verify the veracity of the legal arguments contained in the amended briefing, which continue to contain misstatements of law."

Three Threads Running Through Both Orders

Read together, Miller and Couvrette point to the same three points of failure — and they are ones I’ve talked about and written about, but they are the ones we aren’t doing a good enough job covering in most CLEs.

1. The Hallucination Is Sanctionable, But The Cover-up? It's Career-Altering.

Both courts went out of their way to separate using an hallucinated case versus covering that fact up. Judge Mooty imposed a public reprimand for the false quotations alone — a serious but survivable sanction. The career-ending sanctions (disqualification, six-month suspension, bar referral) resulted from the deletion and the story that surreptitiously changed. Judge Clarke followed the same logic. The briefs themselves earned fees and fines, but the dismissal with prejudice — "terminating sanctions" — came from the lack of candor and the attempted cover-up.

2. Your Chat History Might Become Evidence Against You.

Here’s another thing we don’t cover nearly enough in CLE’s, bar opinions, or friendly conversations. The moment a court raises an AI question, the lawyer's chat history becomes subject to the same preservation duty as any other electronically stored information. Miller applies the Eleventh Circuit's bad-faith spoliation framework to a ChatGPT account and draws an adverse inference. Practitioners should assume that deleting a chat history after AI use is questioned will be treated, at minimum, as bad-faith spoliation under Rule 37(e)(2) and the court's inherent authority — regardless of jurisdiction. This, of course, immediately causes me to point that the best AI use is based on the most solid Data Governance. But that, of course, is an article or CLE for another day.

3. Local Counsel’s Signature Is Not Just a Pro Forma Requirement.

Murphy showed up and signed the documents. That is a common, routine way for local counsel to make a bit of extra money. It’s essentially been a pro forma act. A rubber stamp. But now after Murphy’s sanction in Couvrette is the death knell for just signing as a matter of course or procedure.

"Procedural" local counsel is a fiction we may be living under, but that’s almost certainly not how it was originally intended. And the court declined to accept it.  This will not be the last case where local counsel is sanctioned for signing a brief with AI-generated falsity.  If you sponsor a pro hac vice admission and sign on as local counsel, you now own a real obligation to supervise because that obligation might be enforced, as it was here, with a loss of tens of thousands of dollars.  I’m curious how much Murphy originally received to act as local counsel.

Best Practices -- 7 Practical Rules Every Firm Should Adopt

None of these require a new platform purchase. And, all can be implemented at a solo or small-firm level.

1. Adopt a written cite-verification SOP. Add to your AI Policy that every AI-assisted brief gets two-person verification. The drafter pulls the underlying authority and highlights the quoted language; a second attorney, paralegal or assistant checks against the source. No filing leaves the firm unless every quotation and every case has been independently confirmed in a primary source. At the very least, go through these suggestions, such as running your final brief through Perplexity and asking it to verify your cites and quotes (no, that is not a substitute for human verification, but it’s better than nothing).

2. Treat your AI chat history as evidence the moment a court raises the issue. It’s good data governance to delete confidential or sensitive chats the minute you are finished with them. But it’s spoliation to do so after a potential problem is raised. Larger firms should route deletion authority through a partner — not the lawyer under inquiry.

3. Add a Clause to Your AI Policy. Decide now, in writing, how the firm responds the first time a court flags a suspect citation. My suggestion is a same-day acknowledgement and apology, a written declaration of how the brief was generated, and full production of AI logs. Both judges in these cases said the response that would have preserved the lawyer's career was contrition and disclosure on day one. And we’ve seen that in other cases.

4. Consider the Quality and Settings of the Tool You are Using. ChatGPT Plus is a consumer product. For client work, use tools with zero-data-retention, tenant isolation, and obtainable audit logs — not because they hallucinate less, but because the audit trail proves you did the verification work when the court asks.

5. Add an AI certification block to every brief. Track the federal trend: 30+ district courts and 300+ individual judges now require some form of AI disclosure or certification. A standing internal form — "AI used: yes/no; tool: ___; verification performed: ___; verifier: ___" — protects the file, satisfies most standing orders, and adds more force behind what should be happening anyway.

6. Change your local-counsel and pro hac vice Agreements. If you are local counsel, write into the engagement what supervision you will actually perform on filings — and price the matter accordingly. If you are visiting counsel, expect (and welcome) a real review, but don’t count on it, unless you are paying local counsel accordingly.

7. Train associates and staff on the candor rule. Many (and I suspect most) AI sanctions cases start with a paralegal or associate cutting a corner under deadline pressure. Train the whole team that the right response to "I think I may have failed to check a cite" is to raise it to a partner the same day. Build double checking all cites into your firm training.

Bottom Line

The AI hallucination problem is now well past its novelty phase. In fact, I’ve stopped feeling so incredulous when I see a new case of fake cites. Courts are moving quickly from "caution" to "escalating consequences." And in my experience there is generally nothing a judge or court hates more than lawyer misconduct and lawyers responding disingenuously to the court. As bad as hallucinations, fake case cites, and fake quotes are, the coverup is always worse.


© 2026 Amy Swaner. All Rights Reserved.  May use with attribution and link to article.

Two recent federal court cases show that AI-generated fake citations are only the beginning—the most severe, career-altering consequences come when lawyers fail to verify, disclose, and own their mistakes.

Read

AI Resource

Quick Reference: Risk Mitigation Checklist for AI Notetaker & Transcription Bots

AI in Legal Practice

Jan 2026

Use this one page checklist to minimize risks for you and your clients when considering AI Notetaker and Transcription Bots.

This easy reference covers bots in meetings, and vendor selection.

Share directly with your clients.

Download

Real-World Knowledge

Real-World Knowledge

Steeped in legal and AI expertise, we offer actionable strategies and expert training to help law firms confidently navigate AI's complexities.

Steeped in legal and AI expertise, we offer actionable strategies and expert training to help law firms confidently navigate AI's complexities.

Lawyer to Lawyer

Lawyer to Lawyer

Legal expertise paired with cutting-edge AI strategy to decode compliance, ethics, and complexity — from someone who has practiced.

Legal expertise paired with cutting-edge AI strategy to decode compliance, ethics, and complexity — from someone who has practiced.

Attorney Decides

Attorney Decides

No tool affiliations, no empty theories. Our products and guidance keep the attorney in the decision seat — AI flags, you decide.

No tool affiliations, no empty theories. Our products and guidance keep the attorney in the decision seat — AI flags, you decide.

AI-powered client intake

Engage Gives You Your Time Back.

Let AI do what AI does best, so you can do what you do best. Our flagship product is an AI-powered intake assistant built around the Model Rules. 24/7 client conversations, automatic conflict screening, customized to your practice areas, and white-labeled to your firm.

24/7 Client Intake

Automatic Conflict Checks

White-label to your Firm

From $75 / Attorney / Month

See Engage

Start Free Trial

Expert Guidance for your firm

Customized Solutions to Transform your Practice

Our AI consulting services help law firms thrive in the modern legal landscape. Whether you're integrating AI tools into your workflows or building smarter, safer processes, we deliver solutions tailored to your firm.

Engagements typically run 4-12 weeks and start with a one-day AI readiness audit.

Schedule a Scoping Call

AI for Lawyers

A Working Toolkit for AI in Law

Free, lawyer-written resources on the latest AI developments — privilege, confidentiality, vendor due diligence, bar-rule mapping, and the practical questions every firm is now facing. Long-form articles, working templates, and a glossary built for legal professionals.

Read Articles

Free Resources

AI in Legal Practice

Calculating...

Small and solo practice lawyers make up the majority of the U.S. bar. But they aren’t the largest group of AI adopters. In fact, just the opposite.  In just one year, law firm adoption of AI tools jumped from 14% to 26%, according to a recent Thomson Reuters survey of more than 1,700 legal professionals. Yet the benefits are not evenly distributed: a 2025 Federal Bar Association report found that firms with 51 or more lawyers report a 39% generative AI adoption rate, compared to roughly 20% among firms with 50 or fewer lawyers. At the same time, there are signs of real momentum in the solo and small firm segment itself—Smokeball’s 2025 State of Law Report shows that 53% of small firms and solo practitioners now integrate generative AI into their workflows, up from just 27% in 2023.

Firms of all sizes are experimenting with AI tools for research, drafting, and document review. But the gains are not evenly distributed. Solo practitioners and small firms—the lawyers who make up the majority of the U.S. bar—are adopting AI more slowly than their larger counterparts, and the gap is widening.

That gap is not a function of technophobia. Solo and small-firm lawyers are practical operators who weigh every investment against immediate demands on their time and budget. The real story is structural: these firms face at least five distinct constraints that make adoption harder, even when the underlying technology is sound. Understanding those constraints—and designing products and policies around them—is the first step toward closing the gap and ensuring that AI’s benefits reach the lawyers who serve the widest cross-section of the public.

The 5 Bottlenecks to Small Firm AI Adoption

1. Time and Staffing

Large firms have innovation teams, knowledge managers, and dedicated IT staff to evaluate, pilot, and roll out new tools. A five-lawyer shop has none of that. Every hour spent testing software comes directly out of billable work, business development, or personal time. That makes even a promising AI product feel expensive before any specific AI tool enters the picture.

The math is unforgiving. A solo who bills at $250 an hour and spends ten hours evaluating and investigating a tool has already invested $2,500 in opportunity cost—before deciding whether to subscribe. Larger firms can amortize that evaluation cost across dozens of timekeepers. Solos cannot – they are too busy being everything to everyone. And the evaluation burden does not end with the initial trial. Keeping up with product updates, learning new features, and troubleshooting integrations all fall on the same person who is also appearing in court and returning client calls.

This is not a problem that desire, ambition, or good intention solves. It is a real-life constraint. Every hour spent on considering, setting up, and modifying, is quite literally money out of a solo’s pocket.

2. Enterprise Packaging Creates a Small-Firm Tax

Many legal AI products have been designed with larger organizations in mind. High per-seat pricing, complex onboarding sequences, multi-step implementation plans, and workflow assumptions built around teams of paralegals and associates can make small-firm buyers hesitate. If a tool feels like “one more system to manage” instead of a simple workflow improvement, adoption slows or stalls entirely.

The packaging problem is more than cosmetic. When a product requires a firm to restructure its workflows before seeing value, it asks for an upfront investment that most small firms are unwilling—or unable—to make. Annual contracts, minimum seat counts, and mandatory training sessions may be standard practice for enterprise sales, but they are deal-breakers for a solo weighing a $200-per-month commitment against this month’s overhead.

The tools that gain traction in this market segment tend to be the ones that meet lawyers where they already work. For example, inside their existing email client, document editor, or practice management system. Minimal friction at the point of entry matters more than breadth of features.

3. Risk, Ethics, and Trust Demand a Higher Bar

Confidentiality, hallucinations, supervision obligations, and malpractice exposure are not abstract concerns for solo and small-firm lawyers. They are daily risk-management realities. Larger firms can develop formal AI-use policies, assign associates to verify outputs, and negotiate enterprise data-processing agreements with vendors. A solo practitioner making these judgments alone must proceed carefully.

The regulatory landscape adds complexity. Bar associations and courts are still developing guidance on AI use in practice. Lawyers must supervise AI outputs and cannot delegate professional judgment to a machine, but it places a proportionally heavier compliance burden on lawyers who lack support staff. A solo must personally review every AI-generated document for accuracy, completeness, and ethical compliance. There is no one to share that load with.

4. Minimal Tech Stack and Change Fatigue

AI is an advanced layer. It works best on top of a solid operational foundation: reliable document management, structured data, and consistent workflows. Many solo and small firms still operate on a relatively light tech stack—sometimes little more than email, a word processor, and a billing tool. Layering AI onto incomplete systems produces disappointing results and reinforces skepticism about the technology itself.

Change fatigue compounds the problem. A lawyer who is simultaneously handling intake, marketing, billing, case management, and substantive legal work has limited tolerance for products that require significant workflow redesign. These practitioners have often been through multiple rounds of “transformative” technology promises—cloud migration, paperless offices, automated billing—each of which delivered less than advertised. Understandably, they approach this new technology with a healthy dose of skepticism.

Unless the setup is easy and immediate with visible usefulness, a new technology loses its appeal quickly.

5. Noise and Unclear ROI

The legal AI market is crowded and growing noisier by the quarter. Distinguishing real workflow value from marketing hype takes time and discernment that small-firm lawyers often cannot spare. Many firms experiment with general-purpose AI tools like ChatGPT for quick research or drafting assistance, but they hesitate to commit to specialized legal products unless the return on investment is obvious and fast.

Unclear ROI is one of the biggest reasons evaluation never becomes adoption. When a product cannot show a solo practitioner exactly how it saves time or money within the first week of use, it is unlikely to survive the trial period, if it even makes it that far. Vendors that lead with measurable outcomes—minutes saved per task, reduction in turnaround time, fewer administrative hours per week—fare better than those selling vague transformation narratives or long-horizon productivity gains.

The most effective marketing in this space is not marketing at all. It is a peer recommendation from another solo who tried the tool and can quantify the benefit.

Why Small Firms and Solo Practitioners Should Take the Time

Despite these challenges and barriers, solo and small firms should absolutely take the time to find and implement the right AI tools.  Because when they do, the results are worth the time. Faster intake processing, drafting assistance that cuts first-draft time in half, automated transcription of client calls, and streamlined client communication are among the use cases delivering the clearest returns. These are narrow, practical applications tied to concrete economic value—not aspirational ideas to “reinvent” legal practice.

AI can level the playing field for small firm and solo practitioners. Especially AI that handles non-billable, routine work. In practice areas where responsiveness drives client acquisition—personal injury, family law, immigration, small business—that edge holds even more importance.

Products designed around a single painful workflow consistently outperform broad “AI for everything” platforms in the small-firm segment. A tool that solves one problem well is more valuable to a time-pressed solo than a Swiss Army knife that requires weeks to configure. The winning approach is depth on a specific pain point, not breadth across a dozen features most users will never touch.

Conclusion

Solo and small-firm lawyers do not need more AI hype. Ok, no one needs more AI hype. But what solo and small firm lawyers need are practical, trustworthy tools that solve a concrete problem quickly and visibly. This will probably look like incremental adoption, rather than trying to transform the entire practice overnight.

That incremental path is an intelligent response to real constraints. The vendors, bar associations, and regulators who understand that—and build accordingly—will shape what AI adoption actually looks like for the majority ofthe practicing bar.

© 2026 Amy Swaner. All Rights Reserved.  May use with attribution and link to article.

Why Solo and Small Law Firms Lag in AI Adoption—And How to Change That

Data Privacy and Ethics

Calculating...

Privacy Problems with Generative AI 

Renown AI and privacy expert Daniel J. Solove recently published a paper discussing Generative AI in regard to privacy concerns. 1 Generative AI, while transformative, presents significant privacy challenges.  Solove identified three specific areas of concern: personal data being used by AI, potentially misleading information created by AI, and AI’s ability to undermine fairness and due process.  His paper made me re-think our approach to AI regulation and privacy laws. 

DATA RECONSTITUTION 

Most people using AI presumably understand that privacy concerns are inextricably linked with AI developments—these concerns are widely discussed, recognized, and written about.  But far fewer people are aware of the subtle ways they may inadvertently share personal identifying information (PII) when using AI tools, even with supposedly 'anonymized' data. One of the primary concerns is AI’s generation of new personal data through inferences. GenAI consumes personal data, but it also produces additional data, and can link several sources of private information, often revealing sensitive details that were not initially evident, or were not evident when they were used individually but together are identifiable.  I call this data reconstitution. So even if you are confident you are not sharing personal details, you might still be inadvertently sharing information that can be “reconstituted” to reveal confidential information.  This blurring of lines between data collection and processing circumvents traditional privacy protections and leaves individuals with little control over the information organizations can infer about them.  Even when consumers and individuals have the opportunity to opt out of data collection, there is no way for us to opt out of data inference. 

FAKES AND DEEP FAKES 

Another vein of privacy concern centers around GenAI’s potential for creating malevolent material. GenAI can generate misleading or harmful content, such as deepfakes or false information, which can be used to deceive and manipulate individuals. This capability exacerbates existing privacy concerns by facilitating the spread of misinformation and enabling malicious activities.  For example, AI can be wrongfully used to skillfully recreate the voice of someone we recognize, spewing out hate speech, or being used for malicious political gains.

To date, there is no comprehensive federal law (or state law for that matter) that provides adequate protection to individuals against such fakes and deep fakes. Moreover, the dynamic and opaque nature of Generative AI algorithms poses significant transparency challenges. Understanding these algorithms requires access to the training data, which is often inaccessible or incomprehensible to the general public. This lack of transparency makes it difficult for regulatory bodies to oversee AI systems, and comparably difficult for individuals to trust AI systems. 

DUE PROCESS AND FAIRNESS 

Finally, Generative AI can undermine due process and fairness. AI-generated decisions often lack meaningful avenues for individuals to challenge them. This can lead to situations where people are subjected to decisions that significantly impact their lives without adequate recourse to seek redress or challenge the accuracy and fairness of those decisions. 

This issue can have profound implications across various sectors, including criminal justice, employment, and finance. For example, in the criminal justice system, AI-powered risk assessment tools are increasingly being used to inform decisions about bail, sentencing, and parole. A notable case is the use of the COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) system in several U.S. states. 

In 2016, an investigative report by ProPublica found that the COMPAS system, which predicts the likelihood of a criminal reoffending, was biased against Black defendants. The system was more likely to falsely flag Black defendants as future criminals, wrongly labeling them as high risk nearly twice as often as white defendants. Conversely, white defendants were more likely to be incorrectly labeled as low risk. 

This case highlights several critical issues: 

  1. Opacity: The algorithmic decision-making process was not transparent, making it difficult for defendants to understand or challenge the assessments. 

  1. Bias: The AI system appeared to perpetuate and potentially amplify existing societal biases. 

  1. Lack of due process: Defendants had limited ability to contest these AI-generated risk scores, which significantly influenced their treatment in the justice system. 

  1. Far-reaching consequences: These AI-driven decisions had profound impacts on individuals' lives, affecting their liberty and future prospects. 

The COMPAS case underscores the urgent need for safeguards and oversight in AI systems, especially those used in high-stakes decision-making processes. It highlights the importance of transparency, fairness, and the right to contest AI-generated outcomes. 

To address these concerns, policymakers and AI developers must work towards creating systems that are not only accurate but also fair, transparent, and accountable. This could involve regular audits of AI systems, diverse representation in AI development teams, and clear mechanisms for individuals to challenge AI-driven decisions that affect them. 

Moreover, there's a growing call for "algorithmic impact assessments" - systematic evaluations of AI systems before their deployment to identify potential biases and negative impacts. Such assessments could help prevent unfair outcomes and ensure that AI systems enhance, rather than undermine, principles of due process and equal treatment under the law.2

Regulations Solove Suggests and Their Rationale 

Solove emphasizes the need for comprehensive reforms in privacy law to address the unique challenges posed by Generative AI. He argues against "AI exceptionalism," suggesting that privacy issues related to AI should be tackled as part of broader privacy law reforms. This holistic approach ensures that privacy protections are robust and effective across various contexts, not just AI-specific scenarios. 

One of Solove's key recommendations is to reduce the burden on individuals to manage their privacy. Currently it feels to me like the entire burden of protecting my personal information rests squarely on my shoulders, and I am responsible for protecting my information, with very little power or control, and without knowing the rules.  A standout example of this is Meta’s decision to use all of our personal photos, images, and content on Instagram and Facebook for training their AI models.  For those under the egis of the GDPR they can opt out.  It is unnecessarily difficult, but at least there is a possibility of an option.   With those not covered by the GDPR, such as myself and all people living in the United States and many others, we have no option to opt out.  And it is not clear at all how these images and information will be used. Solove critiques this traditional model of privacy self-management, where individuals are expected to make informed decisions about their data. Instead, he advocates for placing more responsibility on organizations, mandating significant obligations to mitigate risks and ensure accountability. This makes a great deal of sense to me, since those very companies are the ones best placed and most incentivized to exploit my information.  It would also put us in better alignment with the GDPR laws and regulations. 

Critics worry that this will have a chilling effect on technological innovation.  However, Solove also supports adopting a harm and risk-based approach to AI regulation. This involves identifying and addressing potential harms and risks associated with AI, both before and after AI tools are deployed. By balancing preventive (ex-ante) and reactive (ex-post) regulatory measures, policymakers can protect privacy without stifling innovation. 

Transparency and accountability are crucial elements of Solove's regulatory framework. He calls for improved mechanisms to ensure that organizations provide clear and accessible information about their AI systems and maintain robust internal and external accountability measures. This helps build trust and ensures compliance with privacy laws. 

Involving diverse stakeholders in the development and regulation of AI is another important recommendation. Solove emphasizes the need to include voices from underrepresented and marginalized communities to ensure that AI systems are fair and equitable. This inclusive approach helps address biases and ensures that the concerns of all affected parties are considered. 

At present, we cannot make our own sweeping changes to privacy laws, or lack of privacy laws.  We can, however, be certain to use best practices when dealing with PII or any sensitive data. 

Best Practices for Using AI in the Workplace 

As lawyers and legal professionals using Generative AI we can follow several best practices to mitigate privacy risks and ensure responsible use of the technology, including the following: 

1. Transparency and Disclosure: Provide clear and accessible information about AI systems, including data sources, training data, and decision-making processes to those in our firm, and to our clients. Transparency builds trust and helps individuals understand how their data is being used. 

2. Minimize Data Collection and Use.  Practice data minimization by collecting only the necessary data for specific purposes. Implement purpose limitations to ensure data is used only for stated objectives and avoid excessive data collection.  For example, if you need a client’s annual income, but you ask for and receive a copy of their entire form 1040 tax return, you are collecting far more personal information than needed.  

3. Obtain Genuine Consent: Ensure informed consent by providing clear, understandable privacy notices to our potential clients. Individuals should be aware of and agree to how their data will be used, including for AI applications. Therefore, before we even undertake representation of clients, we should inform them of how, why, and when their information will be used, and what we are doing to actively protect their data. 

4. Incorporate Privacy by Design. Integrate privacy considerations into the development and deployment of AI systems that we use in our offices, and all other cloud-based technology that we use in our firms, since AI systems are no more vulnerable than any other cloud-based system.  Accordingly, we should use privacy-enhancing technologies and practices, such as anonymization, encryption, and secure data storage. 

5. Implement Accountability Measures: Establish strong internal and external accountability mechanisms.  Have an AI use policy—more on that in my next article. Conduct regular audits, assessments, and impact analyses to identify and mitigate privacy risks. Be prepared to demonstrate compliance with privacy laws to your clients, your insurance carrier, and potentially to a judge or other decision-making body. 

6. Address Bias and Discrimination: Proactively identify and mitigate biases in AI systems.  Bias is implicit in generative AI tools because they are a reflection of the data used to train them, and that data contains various biases.  We need to be uncompromising and unapologetic for our monitoring of discriminatory output, and also discriminatory input. AI can save us as lawyers a great deal of time, but that is only beneficial if we make the effort to regularly test for discriminatory outcomes and implement corrective measures to avoid perpetuating or amplifying existing inequalities and biases. 

8. Enhance Due Process and Remedies: Provide clear avenues for individuals to challenge AI decisions and seek redress for privacy harms. Ensure that individuals' rights are protected and that they have meaningful ways to contest AI-generated outcomes. 

By following these best practices, our law firms can responsibly harness the power of Generative AI while safeguarding privacy and building trust with our clients, and with the courts.  Since so much is dependent on our reputations, responsible use of AI is a baseline action, not an added measure, to ensure we maintain the highest standards of professional integrity and client trust.  

We as a society are impatiently waiting for comprehensive laws to guide GenAI use, and data privacy.  Until those laws are implemented, these measures aligns with Solove's broader recommendations for comprehensive privacy law reforms and effective regulatory frameworks to manage the complexities of AI. 

1Artificial Intelligence and Privacy, GWU Legal Studies Research Paper No. 2024-36, Daniel J. Solove


The Evolution of Privacy Law in the Age of AI, and Best Practices for Using AI in Your Workplace

AI Tools and Techniques

Calculating...

Executive Summary

AI tools are here to stay. Understanding their distinct "personalities" is essential to competent and strategic use. This article explores how AI personalities—shaped by training data, model architecture, and developer intent—impact legal outcomes, from drafting style to ethical reasoning. Drawing comparisons among leading AI tools such as ChatGPT, Claude, Gemini, Perplexity, Grok, and open-source models, the article offers practical guidance for matching the right AI to the right legal task. By recognizing and leveraging these differences, legal professionals can enhance accuracy, creativity, compliance, and client satisfaction—while mitigating the risks of over-reliance or misalignment. AI is no longer a one-size-fits-all assistant; choosing wisely is now a matter of legal judgment.

_________________________________________________________

At the end of a recent presentation on AI to a group of local government officials, I was asked, "what is the best AI tool?" My lawyer training kicked in and I immediately responded: "It depends." This wasn’t evasion—it was precision. In the precision-driven world of legal work, allAI tools are not created equal. What separates one large language model from another isn't merely technical capability—it's personality by design. These distinct AI "personalities" can produce dramatically different legal work products: from risk-averse contract language to creative settlement frameworks, from meticulously cited research to persuasive argumentation. The difference can impact case outcomes, client satisfaction, and even ethical compliance.

The differences in LLMs manifest as personalities that directly influence drafting style, risk tolerance, and analytical approach. Understanding these nuances isn't just interesting—it's becoming essential to competent representation in an AI-augmented legal landscape. This article examines how AI personalities emerge from architecture, training, and design intent, and provides practical guidance for selecting the right digital assistant for your specific legal tasks.

Why AI Tools Differ: Data, Algorithms, and Purpose

Three core factors shape every generative AI model. I’ve discussed these three factors in several other articles, so I’ll keep it short here.

Training Data– The information used to train the model, in other words, what the model "learns" from, affects everything from legal knowledge to tone. Some tools are trained on broad internet data; others include specialized legal, academic, or scientific texts. This data forms the foundation of the AI's knowledge base and influences the accuracy and relevance of its outputs.

Underlying Architecture—In an LLM, the algorithm is the set of mathematical procedures and rules that govern how the model processes inputs and generates outputs. It’s the engine behind the tool’s ability to understand language, reason about it, and produce coherent, context-appropriate responses.The model's algorithm affects reasoning ability, hallucination rates, and how it balances creativity with caution. Some algorithms are optimized for long-context memory or symbolic reasoning, while others are optimized for speed and resource efficiency.

Design Intent and Safety Protocols–Guardrails, default prompts, and content filters all shape how the AI behaves in practice. A model designed for creative brainstorming will act very differently than one tuned for precision research or ethical deliberation.

Behind the scenes, every model runs on hidden instructions—called system prompts—that set the tone, priorities, and boundaries of an AI tool.

  • Safety filters: These limit what the model is allowed to say—especially about controversial or high-risk topics. Case in point: DeepSeek being prohibited from discussing topics that the Chinese Government would find uncomfortable.

  • Voice and persona: Some tools (ChatGPT, Claude) are designed to be personable; others (Perplexity) are intentionally minimal.

  • Governance objectives: Anthropic programs Claude to follow a constitutional “code of conduct.” xAI’s Grok minimizes restrictions to promote expressive freedom.

Together, these elements give rise to what many users describe as an AI tool’s "personality."

AI Personality as a Reflection of Vision

Even though AI models are not sentient and have no emotional self-awareness, users regularly perceive them as having distinct personalities. This isn't accidental. It's a result of deliberate decisions by developers about how the tool should behave. Each major AI tool essentially expresses its creator’s vision for what AI should be, and those visions diverge meaningfully.

OpenAI (ChatGPT):

We continue to believe that the best way to make an AI system safe is by iteratively and gradually releasing it into the world, giving society time to adapt and co-evolve with the technology, learning from experience, and continuing to make the technology - Sam Altman

OpenAI's mission centers on ensuring that AGI benefits all of humanity. ChatGPT reflects this in its design: helpful, friendly, cautious, and broadly capable. It aims for alignment with user intent while maintaining a highly moderated, safety-first posture. It strives to be a reliable assistant in almost any context, but sometimes hesitates in nuanced or high-risk domains.

Anthropic (Claude):

The vision of AI as a guarantor of liberty, individual rights, and equality under the law is too powerful a vision not to fight for. — Dario Amodei

Claude is built with "Constitutional AI," a framework that encourages the model to reason ethically and transparently. This gives Claude a reflective, principled tone. It often feels like a thoughtful counselor—ideal for lawyers working on ethical dilemmas, AI policy, or complex compliance matters. Its design is shaped by Anthropic's belief that AI should be fundamentally safe, interpretable, and grounded in human values.

Google DeepMind (Gemini):

For a long time, we’ve been working towards a universal AI agent that can be truly helpful in everyday life. - Demis Hassabis

Gemini reflects Google's legacy as a search and information company. Its personality is efficient, structured, and knowledge-driven. Gemini often avoids embellishment or speculation, favoring clean, fact-based responses. While it may feel less personal or imaginative, it excels at surfacing relevant data quickly—especially when integrated with Google’s suite of tools. Gemini is best understood as a highly competent knowledge worker: focused, fast, and efficient.

Perplexity:

“The journey of Perplexity began with a leap of faith. We built the platform prioritising accuracy and transparency.” - Aravind Srivanas

Purpose-built as an "answer engine," Perplexity is pragmatic and direct. It doesn’t engage in creative dialogue or philosophical reflection. Instead, it returns clear answers with citations, acting more like a high-speed research librarian than an assistant. This utilitarian ethos reflects a belief that transparency and speed are paramount.

xAI (Grok):

“The good future of AI is one of immense prosperity where there is an age of abundance; no shortage of goods and services.” - Elon Musk

Grok, developed by Elon Musk’s xAI and integrated into X (formerly Twitter), presents a more irreverent, edgy personality. It is designed to be humorous, bold, and occasionally provocative—emphasizing freedom of expression and fewer content restrictions. Grok feels more like a contrarian intern than a polished assistant, which may appeal to users seeking unfiltered dialogue. However, this tone is less suited to professional or regulated legal work unless handled with great care.

These different visions shape not only what the tools can do, but also how they feel to use. And that feeling matters, especially in legal work that demands both trust and precision. So how do the tools apply to legal work, here is a general guide, based on my personal observations and investigation.

AI Tool Personalities: A Guide for Legal Professionals

1. OpenAI (ChatGPT) _______________________________________

Core Personality Traits:

  • Friendly, careful, helpful, versatile

  • Balanced between creativity and caution

  • Polite with visible hedging or disclaimers

  • Conflict-avoidant and generally neutral in tone

Implications: OpenAI wants its models to be general-purpose assistants: safe for everyday users but capable enough for professionals. It's walking a fine line between helpfulness and containment. This results in a personality that is measured, moderate, and neutral unless fine-tuned otherwise (e.g., via custom GPTs).

Best Legal Uses:

  • Creative brainstorming (marketing content, slogans)

  • General legal drafting (with custom instructions)

  • Client communication templates

  • Reviewing contracts and identifying red flags

  • Summarizing discovery or deposition transcripts

2. Anthropic (Claude)______________________________________

Core Personality Traits:

  • Ethical, reflective, deferential, emotionally intelligent

  • More philosophical than productivity-focused

  • Prioritizes moral consistency and safety reasoning

  • Measured, thoughtful, and nuanced in responses

Implications: Claude is designed to avoid manipulation, deception, and misuse by grounding its responses in a visible set of principles. Its personality reflects moral agency, sometimes at the expense of assertiveness or creativity. It's ideal when you want an AI that prioritizes safety before cleverness.

Best Legal Uses:

  • Ethical guidance and AI policy brainstorming

  • Drafting internal firm policies or compliance materials

  • Client communications requiring emotional intelligence

  • Creative brainstorming with ethical nuance

  • Creating CLE presentations or legal training materials


    3. Google DeepMind (Gemini)________________________________

Core Personality Traits:

  • Efficient and information-rich

  • Integrated and context-aware

  • Neutral and guarded in tone

  • Less personality-driven, more utilitarian

  • Fact-based with structured outputs

Implications: Gemini reflects Google's legacy as a search and information company. Its personality is efficient, structured, and knowledge-driven. It excels at surfacing relevant data quickly—especially when integrated with Google's suite of tools. Gemini is best understood as a highly competent knowledge worker: focused, fast, and efficient.

Best Legal Uses:

  • Legal research requiring factual citations

  • Fast factual queries about legal matters

  • Reviewing contracts for specific data points

  • Information extraction from complex documents

  • Integration with existing Google Workspace documents

4. Perplexity ______________________________________________

Core Personality Traits:

  • Direct, concise, no-frills, source-focused

  • Utilitarian and pragmatic in approach

  • Minimal speculation or creative embellishment

  • Citation-driven and transparent

Implications: Perplexity's personality is shaped by its goal to replace or augment search engines, not your assistant. It doesn't try to sound empathetic or chatty; it tries to show its work. That utilitarian approach results in a personality that feels more like a high-speed research librarian.

Best Legal Uses:

  • Legal research requiring extensive citations

  • Fast factual queries with minimal verbosity

  • Finding relevant case law and precedents

  • Due diligence research on companies or individuals

  • Gathering evidence-based information quickly

5. xAI (Grok)______________________________________________

Core Personality Traits:

  • Irreverent, edgy, bold, occasionally provocative

  • Humorous and contrarian in tone

  • Fewer content restrictions than competitors

  • Resembles a contrarian intern more than a polished assistant

Implications: Grok's personality may appeal to users seeking unfiltered dialogue or creative brainstorming outside conventional boundaries. However, this tone is less suited to professional or regulated legal work unless handled with great care. It presents higher reputational risks in formal settings.

Best Legal Uses:

  • Brainstorming unconventional legal strategies

  • Generating alternative perspectives on legal problems

  • Informal research or exploration

  • Testing arguments against potential counterpoints

  • Internal creative sessions (with appropriate oversight)


Open Source Models

1. Mistral/LLaMA __________________________________________

Core Personality Traits:

  • Lean, powerful, and unopinionated (unless fine-tuned)

  • Minimalist engineering ethos

  • Highly customizable based on implementation

  • Generally neutral without specific personality defaults

Corporate Vision: Open-source models reflect the minimalist engineering ethos of their communities: lean, powerful, and unopinionated—unless fine-tuned. They prioritize flexibility, customization, and community-driven development.

Implications: These models allow for maximum customization to specific legal needs but require more technical expertise to implement effectively. They provide greater control over data privacy and can be deployed in air-gapped environments for sensitive legal work.

Best Legal Uses:

  • Self-hosted solutions for confidential legal matters

  • Custom-tuned applications for specific practice areas

  • Integration into existing legal workflow systems

  • Situations requiring full control over AI training and usage

  • Specialized legal document analysis with custom training

2. DeepSeek ______________________________________________

Core Personality Traits:

  • Academic and research-oriented

  • Methodical and precise in reasoning

  • Strong technical foundation with mathematical capabilities

  • Balanced between helpfulness and caution

  • Generally neutral and objective in tone

Corporate Vision: DeepSeek aims to "seek truth from facts" with a mission focused on advancing frontier AI research while making powerful models accessible. Founded by former researchers from top AI labs, DeepSeek emphasizes both cutting-edge capabilities and responsible deployment of AI technology.

Implications: DeepSeek's personality reflects its research origins, making it particularly well-suited for technically complex legal work requiring methodical reasoning. Its approach balances innovation with responsibility, producing responses that are technically precise while maintaining appropriate professional boundaries. The model excels at tasks requiring systematic thinking and technical accuracy.

Best Legal Uses:

  • Analysis of complex regulatory frameworks

  • Patent law research and technical documentation

  • Reasoning through intricate legal problems step-by-step

  • Financial and tax law applications requiring mathematical precision

  • Research-intensive legal projects requiring methodical approaches

Hallucination Rates and Legal Accuracy

The tendency to "hallucinate" (generate plausible but factually incorrect information) varies significantly across AI platforms, with critical implications for legal work:

Hallucination Risk Comparison:

  • Claude: Generally exhibits lower hallucination rates when discussing legal principles, due to its constitutional AI framework that encourages epistemic humility. Claude typically acknowledges uncertainty rather than inventing details, making it slightly safer for preliminary legal analysis.

  • ChatGPT: Shows higher variance in hallucination rates depending on the version used. GPT-4o demonstrates improved reliability over earlier versions but still occasionally fabricates case citations or statute numbers, particularly when pushed beyond its knowledge boundaries.

  • Gemini: Tends toward lower hallucination rates when discussing factual legal information within its training corpus but may struggle with jurisdiction-specific nuances. Its integration with Google's search capabilities can mitigate some risks.

  • Perplexity: By combining generative AI with search functionality, Perplexity reduces hallucination risks for recent legal developments. However, its synthesis of multiple sources can occasionally create misleading impressions of legal consensus where genuine disputes exist.

_________________________________________________________

Best Practices and Risk Mitigation Strategies:

  1. Require AI tools to provide specific citations for all legal claims.

  2. Cross-verify AI-generated legal information across multiple platforms (eg, use Gemini to check Claude’s output).

  3. Keep the Human in the loop; Use AI outputs as starting points rather than authoritative sources.

  4. Verify every legal citation, even if you are using a legal-specific tool. AI tools—even legal-specific ones—are not experts in nuance. Make certain that the cited case is a real case, and actually stands for the proposition you’re citing it for.

  5. Develop prompt techniques that explicitly discourage speculation in areas of uncertainty.

_________________________________________________________

Choosing the Right Tool for the Task

With this context in mind, lawyers can make smarter choices about which tool to use based on the task at hand. Below is a quick reference guide:

Conclusion: Know the Tool, Know the Task

In the same way lawyers choose the right precedent or statute for a given case, choosing the right AI tool can dramatically improve outcomes. Rather than asking which AI is “best,” we should be asking: Best for what?

Distinct AI "personalities" represent more than a quirk of engineering—it offers a strategic advantage for lawyers who understand how to leverage these differences. Just as a skilled attorney selects the right specialist for different aspects of a case, tomorrow's legal professionals must develop fluency in matching AI tools to specific legal tasks. And the most important tool — your professional judgment. The ultimate responsibility for legal work remains with the attorney.

Personality by Design: Matching AI Tools to Legal Tasks

Master AI at your own pace

On-Demand Training for Professionals

Comprehensive, self-paced training for lawyers and executives. Covers AI risk management, Model-Rule-compliant use of AI tools, and governance frameworks for AI-enabled firms.

CLE-eligible in select jurisdictions

Browse Course

Free Monthly Newsletter

Stay Current.

Stay Current.

Transform your legal practice with AI—sign up for our free emails and gain exclusive, actionable insights designed for lawyers and law firms.

Transform your legal practice with AI—sign up for our free emails and gain exclusive, actionable insights designed for lawyers and law firms.

AI training, consulting and tools for law firms. Built by lawyers, engineered for legal ethics.

Lexara Consulting, LLC · Iowa · © 2026

Engage

Pricing

Demo

Integrations

Consulting

Courses

Speaking

Articles

Glossary

Toolkit

Security

Privacy

Terms

Responsible AI

Lexara provides legal-adjacent consulting, training, and software. Engaging Lexara does not create an attorney–client relationship, and the services described on this site are not the practice of law. See Iowa R. Prof'l Conduct 32:5.7.

AI training, consulting and tools for law firms. Built by lawyers, engineered for legal ethics.

Lexara Consulting, LLC · Iowa · © 2026

Engage

Pricing

Demo

Integrations

Consulting

Courses

Speaking

Articles

Glossary

Toolkit

Security

Privacy

Terms

Responsible AI

Lexara provides legal-adjacent consulting, training, and software. Engaging Lexara does not create an attorney–client relationship, and the services described on this site are not the practice of law. See Iowa R. Prof'l Conduct 32:5.7.

AI training, consulting and tools for law firms. Built by lawyers, engineered for legal ethics.

Lexara Consulting, LLC · Iowa · © 2026

Engage

Pricing

Demo

Integrations

Consulting

Courses

Speaking

Articles

Glossary

Toolkit

Security

Privacy

Terms

Responsible AI

Lexara provides legal-adjacent consulting, training, and software. Engaging Lexara does not create an attorney–client relationship, and the services described on this site are not the practice of law. See Iowa R. Prof'l Conduct 32:5.7.

AI training, consulting and tools for law firms. Built by lawyers, engineered for legal ethics.

Lexara Consulting, LLC · Iowa · © 2026

Engage

Pricing

Demo

Integrations

Consulting

Courses

Speaking

Articles

Glossary

Toolkit

Security

Privacy

Terms

Responsible AI

Lexara provides legal-adjacent consulting, training, and software. Engaging Lexara does not create an attorney–client relationship, and the services described on this site are not the practice of law. See Iowa R. Prof'l Conduct 32:5.7.