Category Archives: Law Practice & Procedure

A Master Table of Truth

04 Tuesday Nov 2025

Posted by in ai, Computer Forensics, E-Discovery, General Technology Posts, Law Practice & Procedure, Uncategorized

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Lawyers using AI keep turning up in the news for all the wrong reasons—usually because they filed a brief brimming with cases that don’t exist. The machines didn’t mean to lie. They just did what they’re built to do: write convincingly, not truthfully.

When you ask a large language model (LLM) for cases, it doesn’t search a trustworthy database. It invents one. The result looks fine until a human judge, an opponent or an intern with Westlaw access, checks. That’s when fantasy law meets federal fact.

We call these fictions “hallucinations,” which is a polite way of saying “making shit up;” and though lawyers are duty-bound to catch them before they reach the docket, some don’t. The combination of an approaching deadline and a confident-sounding computer is a dangerous mix.

Perhaps a Useful Guardrail

It struck me recently that the legal profession could borrow a page from the digital forensics world, where we maintain something called the NIST National Software Reference Library (NIST NSRL). The NSRL is a public database of hash values for known software files. When a forensic examiner analyzes a drive, the NSRL helps them skip over familiar system files—Windows dlls and friends—so they can focus on what’s unique or suspicious.

So here’s a thought: what if we had a master table of genuine case citations—a kind of NSRL for case citations?

Picture a big, continually updated, publicly accessible table listing every bona fide reported decision: the case name, reporter, volume, page, court, and year. When your LLM produces Smith v. Jones, 123 F.3d 456 (9th Cir. 2005), your drafting software checks that citation against the table.

If it’s there, fine—it’s probably references a genuine reported case.
If it’s not, flag it for immediate scrutiny.

Think of it as a checksum for truth. A simple way to catch the most common and indefensible kind of AI mischief before it becomes Exhibit A at a disciplinary hearing.

The Obstacles (and There Are Some)

Of course, every neat idea turns messy the moment you try to build it.

Coverage is the first challenge. There are millions of decisions, with new ones arriving daily. Some are published, some are “unpublished” but still precedential, and some live only in online databases. Even if we limited the scope to federal and state appellate courts, keeping the table comprehensive and current would be an unending job; but not an insurmountable obstacle.

Then there’s variation. Lawyers can’t agree on how to cite the same case twice. The same opinion might appear in multiple reporters, each with its own abbreviation. A master table would have to normalize all of that—an ambitious act of citation herding.

And parsing is no small matter. AI tools are notoriously careless about punctuation. A missing comma or swapped parenthesis can turn a real case into a false negative. Conversely, a hallucinated citation that happens to fit a valid pattern could fool the filter, which is why it’s not the sole filter.

Lastly, governance. Who would maintain the thing? Westlaw and Lexis maintain comprehensive citation data, but guard it like Fort Knox. Open projects such as the Caselaw Access Project and the Free Law Project’s CourtListener come close, but they’re not quite designed for this kind of validation task. To make it work, we’d need institutional commitment—perhaps from NIST, the Library of Congress, or a consortium of law libraries—to set standards and keep it alive.

Why Bother?

Because LLMs aren’t going away. Lawyers will keep using them, openly or in secret. The question isn’t whether we’ll use them—it’s how safely and responsibly we can do so.

A public master table of citations could serve as a quiet safeguard in every AI-assisted drafting environment. The AI could automatically check every citation against that canonical list. It wouldn’t guarantee correctness, but it would dramatically reduce the risk of citing fiction. Not coincidentally, it would have prevented most of the public excoriation of careless counsel we’ve seen.

Even a limited version—a federal table, or one covering each state’s highest court—would be progress. Universities, courts, and vendors could all contribute. Every small improvement to verifiability helps keep the profession credible in an era of AI slop, sloppiness and deep fakes.

No Magic Bullet, but a Sensible Shield

Let’s be clear: a master table won’t prevent all hallucinations. A model could still misstate what a case holds, or cite a genuine decision for the wrong proposition. But it would at least help keep the completely fabricated ones from slipping through unchecked.

In forensics, we accept imperfect tools because they narrow uncertainty. This could do the same for AI-drafted legal writing—a simple checksum for reality in a profession that can’t afford to lose touch with it.

If we can build databases to flag counterfeit currency and pirated software, surely we can build one to spot counterfeit law?

Until that day, let’s agree on one ironclad proposition: if you didn’t verify it, don’t file it.

Chambers Guidance: Using AI Large Language Models (LLMs) Wisely and Ethically

19 Thursday Jun 2025

Posted by in ai, General Technology Posts, Law Practice & Procedure

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Tomorrow, I’m delivering a talk to the Texas Second Court of Appeals (Fort Worth), joined by my friend, Lynne Liberato of Houston. We will address LLM use in chambers and in support of appellate practice, where Lynne is a noted authority. I’ll distribute my 2025 primer on Practical Uses for AI and LLMs in Trial Practice, but will also offer something bespoke to the needs of appellate judges and their legal staff–something to-the-point but with cautions crafted to avoid the high profile pitfalls of lawyers who trust but don’t verify.

Courts must develop practical internal standards for the use of LLMs in chambers. These AI applications are too powerful to ignore and too powerful to use without attention given to safe use.

Chambers Guidance: Using AI Large Language Models (LLMs) Wisely and Ethically

Prepared for Second District Court of Appeals (Fort Worth)

Purpose
This document outlines recommended practices for the safe, productive, and ethical use of large language models (LLMs) like ChatGPT-4o in chambers by justices and their legal staff.

I. Core Principles

  1. Human Oversight is Essential
    LLMs may assist with writing, summarization, and idea generation, but should never replace legal reasoning, human editing, or authoritative research.
  2. Confidentiality Must Be Preserved
    Use only secure platforms. Turn off model training/sharing features (“model improvement”) in public platforms or use private/local deployments.
  3. Verification is Non-Negotiable
    Never rely on an LLM for case citations, procedural rules, or holdings without confirming them via Westlaw, Lexis, or court databases.  Every citation is suspect until verified.
  4. Transparency Within Chambers
    Staff should disclose when LLMs were used in a draft or summary, especially if content was heavily generated.  Prompt/output history should be preserved in chambers files.
  5. Judicial Independence and Public Trust
    While internal LLM use may be efficient, it must never undermine public confidence in the independence or impartiality of judicial decision-making. The use of LLMs must not give rise to a perception that core judicial functions have been outsourced to AI.

II. Suitable Uses of LLMs in Chambers

  • Drafting initial outlines of bench memos or summaries of briefs
  • Rewriting judicial prose for clarity, tone, or readability
  • Summarizing long records or extracting procedural chronologies
  • Brainstorming counterarguments or exploring alternative framings
  • Comparing argumentative strength and inconsistencies of and between parties’ briefs

Note: Use of AI output that may materially influence a decision must be identified and reviewed by the judge or supervising attorney.

III. Prohibited or Cautioned Uses

  • Do not insert any LLM-generated citation into a judicial order, opinion, or memo without independent confirmation
  • Do not input sealed or sensitive documents into unsecured platforms
  • Do not use LLMs to weigh legal precedent, assess credibility, or determine binding authority
  • Do not delegate critical judgment or reasoning tasks to the model (e.g., weighing precedent or evaluating credibility)
  • Do not rely on LLMs to generate summaries of legal holdings without human review of the supporting authority

IV. Suggested Prompts for Effective Use

These prompts may be useful when paired with careful human oversight and verification

  • “Summarize this 40-page brief into 5 bullet points, focusing on procedural history.”
  • “Summarize the uploaded transcript respecting the following points….”
  • “Summarize the key holdings and the law in this area”
  • “Rewrite this paragraph for clarity, suitable for a published opinion.”
  • “List potential counterarguments to this position in a Texas appellate context.”
  • “Explain this concept as if to a first-year law student.”

Caution: Prompts seeking legal summaries (e.g., “What is the holding of X?” or “Summarize the law on Y”) are particularly prone to error and must be treated with suspicion. Always verify output against primary legal sources.

V. Public Disclosure and Transparency

Although internal use of LLMs may not require disclosure to parties, courts must be sensitive to the risk that judicial reliance on AI—even as a drafting aid—may be scrutinized. Consider whether and what disclosure may be warranted in rare cases when LLM-generated language substantively shapes a judicial decision.

VI. Final Note

Used wisely, LLMs can save time, increase clarity, and prompt critical thought. Used blindly, they risk error, overreliance, or breach of confidentiality. The justice system demands precision; LLMs can support it—but only under a lawyer’s and judge’s careful eye and hand.

Prepared by Craig Ball and Lynne Liberato, advocating thoughtful AI use in appellate practice.

Of course, the proper arbiters of standards and practices in chambers are the justices themselves; I don’t presume to know better, save to say that any approach that bans LLMs or presupposes AI won’t be used is naive. I hope the modest suggestions above help courts develop sound practical guidance for use of LLMs by judges and staff in ways that promote justice, efficiency and public confidence.

Tailor FRE 502(d) Orders to the Case

20 Monday Jan 2025

Posted by in E-Discovery, Law Practice & Procedure

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Having taught Federal Rule of Evidence 502 (FRE 502) in my law classes for over a decade, I felt I had a firm grasp of its nuances. Yet recent litigation where I serve as Special Master prompted me to revisit the rule with Proustian ‘fresh eyes,’ uncovering insights I hope to share here

I’ve long run with the herd in urging lawyers to “always get a 502 order,” never underscoring important safeguards against unintended outcomes; but lately, I had the opportunity to hear from experienced trial counsel on both sides of a FRE 502 order negotiation and have gained a more nuanced view.

Enacted in 2008, FRE 502 was a means to use the federal rules (and Congress’ adoption of the same) to harmonize widely divergent outcomes vis-à-vis subject matter waiver flowing from the inadvertent disclosure of privileged information. 

That’s a mouthful, and I know many readers aren’t litigators, so let’s lay a little foundation.

Confidential communications shared in the context of special relationships are largely shielded from compulsory disclosure by what is termed “privilege.”  You certainly know of the Fifth Amendment privilege against self-incrimination, and no doubt you’ve heard (if only in crime dramas) that confidential communications between a lawyer and client for the purpose of securing legal advice are privileged.  That’s the “attorney-client privilege.” Other privileges extend to, inter alia, spousal communications, confidences shared between doctor and patient and confidences between clergy and parishioner for spiritual guidance.  None of these privileges are absolute, but that’s a topic for another day. 

Yet another privilege, called “work-product protection,” shields from disclosure an attorney’s mental impressions, conclusions, opinions, or legal theories contained in materials prepared in anticipation of litigation or for trial.  Here, we need only consider the attorney-client privilege and work-product protection because FRE 502 applies exclusively to those two privileges.

Clearly, lawyers enjoy extraordinary and expansive rights to withhold privileged information, and lawyers really, REALLY hate to mess up in ways that impair those rights. I’d venture that as much effort and money is expended seeking to guard against the disclosure of privileged material as is spent trying to isolate relevant evidence. A whole lot, at any rate.

One of the quickest ways to lose a privilege is by sharing the privileged material with someone who isn’t entitled to claim the privilege.  Did the lawyer let the friend who drove the client to the law office sit in when confidences were exchanged?  Such actions waive the privilege.  One way to lose a privilege is by accidentally letting an opponent get a look at privileged material.  That can happen in a host of prosaic ways, even just by the wrong CC on an email.   More often, it’s a consequence of a failed e-discovery process, say, a reviewer or production error.  Inadvertently producing privileged information in discovery is every litigator’s nightmare.  It happens often enough that the various states and federal circuits developed different ways of balancing protection from waiver against findings that the waiver opened the door to further disclosure in a disaster scenario called “Subject Matter Waiver.”

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