[EDRM Editor’s Note: The opinions and positions are those of Craig Ball. This article is republished with permission and was first published here on July 20, 2026.]

Time flies: Two years ago, in a post here, I floated the proposition that if the other side is going to hand your requests for production to a large language model and let the machine decide what’s responsive, then let’s draft those requests with the machine in mind. Doug Austin was generous enough to amplify the idea a week later. Two years on, it’s no longer something to merely think about; producing parties are ceding first-pass relevance review to LLMs.  The request in front of the model is now a prompt whether we know it or not.

So, let’s make that prompt our own.

A large language model doesn’t share the experience a seasoned reviewer brings to “all documents touching or concerning the transaction.” Ambiguity that a human reviewer resolves by instinct becomes, for a model, a coin flip between over- and under-inclusion. If you want their AI to find what you need, then tell it how to discriminate.

Seen that way, an AI-aware request for production is doing one of two things. At a minimum, it lards the request with enough discrete elements—custodians, systems, defined terms, date ranges, document types, examples—that opposing counsel can hardly avoid building those elements into whatever prompt they feed their review platform. At best (insofar as the rules of procedure permit or human sloth promotes), it hands them a fully formed prompt: language so ready to roll that the path of least resistance is to paste it straight away. The first mode constrains by specificity; the second exploits the happy truth that a good prompt ready-made is a prompt somebody will be tempted to deploy. Either way, the benefits are the same: clarity over boilerplate, context over conclusion, defined terms over loose keywords, and illustrative examples that let the model pattern-match to the documents you need.

None of this is way out there. It’s just good drafting, made newly consequential because the first reader is now a machine.

Oh, Those Pesky Rules

The Federal Rules reward this technique. FRCP Rule 34(b)(1)(A) requires that a request “describe with reasonable particularity each item or category of items to be inspected.” Particularity and prompt-craft pull on the same oar: both esteem the concrete over the conclusory. An AI-aware request isn’t a departure from Rule 34; it’s Rule 34 taken seriously by a lawyer who recognizes a model is on the other end.

Cautions to Stay Inside the Guardrails

First, drafting a request is not the same as running the other side’s review.  The Sedona Principles, Third Edition, Principle 6, holds that “responding parties are best situated to evaluate the procedures, methodologies, and technologies appropriate for preserving and producing their own electronically stored information.” I’ve quarreled with Sedona Six before—competence is something a producing party should endeavor to earn, not something we should presume—but the principle still rears its ugly head to shield the tools the other side chooses. So, the line to walk is this: an AI-aware request steers relevance and particularity; it does not dictate the responding party’s platform. Write the request to tell the model, any model, what responsiveness looks like. Don’t write it so as to effectively tell opposing counsel which model to choose or how to configure it. The former is advocacy and fair game. The latter invites a well-founded objection.

Second, particularity is a gun that kicks as hard as it shoots. The more precisely we enumerate document types and search terms, the greater the risk that a producing party treats a careful list as the outer boundary of the request and withholds everything beyond it. Two years ago, I noted this language would be “unlikely to be embraced by counsel ever-apprehensive of framing a request too-narrowly,” and the worry remains. The fix is craftsmanship: pair concrete guidance with a stated purpose and, okay, keep your cherished “including but not limited to,” so your examples instruct the model without unduly shrinking the scope.

There’s a third point worth mentioning, because it proves prompts are becoming discovery objects in their own right. In Conservation Law Foundation, Inc. v. Shell Oil Co., No. 3:21-cv-00933 (D. Conn. May 18, 2026), a magistrate judge ordered production of the prompts an expert used to drive an AI tool, treating them as fair game for discovery into methodology. That case concerns an expert’s prompts, not the language of an RFP (and, frankly, I don’t think the judge got it right in the face of a stipulation between the parties); so don’t read too much into it.  Still, the decision signals that courts have started to regard AI prompts as part of the discovery record. The prompt-craft we bring to our requests and the prompts our adversaries feed their review platforms are drifting toward daylight.

How Does It Work?

Take a matter everyone remembers. In the Dominion Voting Systems’ defamation suit against Fox News—the case that settled for $787.5 million in April 2023—the fight turned on what people inside Fox knew about the falsity of the fraud claims their network kept airing. A conventional, human-oriented request in that case might read like this, and requests like it are served every day:

All Documents and Communications relating to Dominion Voting Systems, including any allegation of fraud, vote manipulation, algorithmic “vote switching,” or foreign influence involving Dominion’s products in connection with the November 2020 U.S. presidential election.

Does a senior associate knows what to do with that? An AI model told to sort a Fox custodian’s mailbox against it will drown—everything “relates to” Dominion in a case about Dominion! A fallback to keywords will be, at once, over- and under-inclusive.

Let’s rewrite the request as something a reviewer will be sorely tempted to paste straight into a review tool:

Identify and produce every Document and Communication—including emails, text and Signal messages, Slack messages, on-air scripts, booking notes, and drafts—in which any Fox News host, producer, booker, or executive discussed, doubted, questioned, promoted, or sought to substantiate the claim that Dominion Voting Systems’ machines or software switched, deleted, or altered votes, or were connected to Smartmatic, Venezuela, Hugo Chávez, or the “Kraken.” The purpose of this request is to surface each custodian’s internal knowledge of the truth or falsity of the on-air fraud allegations. Responsive material will typically originate with custodians including the hosts and executives identified in Schedule A, date from November 1, 2020 through the network’s 2021 on-air corrections, and use terms such as “Dominion,” “Powell,” “Giuliani,” “rigged,” “switch,” “Smartmatic,” “Chávez,” or “crazy.” An example of a responsive document is an internal text message in which a host privately derided the fraud claims as baseless while the network continued to air them.

Notice what the second version does and doesn’t do. It gives the model purpose, custodians, boundaries, defined terms, and an example—everything a prompt needs, and enough discrete hooks that the other side can’t build its own prompt without importing most of them. It says nothing about which review platform to buy or how to configure and train it.

Use the Tools

One last point: If we’re going to draft requests for a machine to read, we might as well let a machine do the heavy lifting. Today’s subscription-tier models—the paid versions of ChatGPT, Claude, Gemini, or ChatGPT, not the free tiers coasting on last year’s tech—handle this conversion with ease. Feed one your draft and the bare contours of the case and let it do the heavy lifting. The prompt need be no fancier than this:

You are an experienced litigator revising a request for production so an AI tool conducting first-pass relevance review will read it accurately. Rewrite the request below to add reasonable particularity: name the likely custodians and data sources, add a one-sentence statement of purpose, enumerate the pertinent document types, list the defined terms and search language, set a sensible date range, and give one example of a responsive document—without dictating the responding party’s review methodology or narrowing the request’s reach. Here’s the request: [paste].

As always, mind the confidentiality of whatever you paste, use an account that won’t train on your inputs, and read every word the machine spits back—but let it spare you the first draft.

The robots are reading our requests now. Shouldn’t we write for our real audience?

Read the original article here.

Assisted by GAI and LLM Technologies per EDRM’s GAI and LLM Policy.