The Request-to-Bates Map: Drafting RFP Responses With AI

Each request, each Bates range, each withheld document: the record an AI-drafted Rule 34 response should be written from.

Since the 2015 amendments to the federal discovery rules, every objection to a request for production must state whether any responsive materials are being withheld on the basis of that objection.

Can AI help draft responses to requests for production? Yes. The rule text kept by Cornell's Legal Information Institute allows a written response within 30 days after service, answered item by item, with objections stated "with specificity." A language model can fill that window with polished prose. It cannot know, from the request alone, what the review set aside.

I call the missing piece a Request-to-Bates Map: a per-request record linking each request for production to its objection, the Bates ranges produced in answer, and the responsive documents withheld, each with its stated ground. Bates numbers already travel into briefs, hearings, and trial. The map carries that discipline backward, from the exhibit to the request that called it forth, the way a star's light leads back to the star.

The rule's drafters meant the withholding sentence to end a familiar confusion. A party objects several times, produces anyway, and leaves the other side unsure whether anything stayed behind.

So the working question for every objection is plain: which documents did this ground keep out, and where is that written down?

Why Does the Withholding Sentence Matter More Once AI Drafts the Response?

AI-assisted review now runs cents per document against roughly $19K/GB for manual review, so the hard part of a Rule 34 response shifts from writing it to knowing what was withheld.

Rule 34(b)(2)(C) is the reason. Before any AI tool drafts a single response, I would settle three things in plain terms:

1. Which requests draw an objection, and on what stated ground.
2. Which produced documents answer each request, by Bates range.
3. Which responsive documents stay out of the production, and on which objection.

The rule itself sets the frame. The text of Federal Rule of Civil Procedure 34 published by Cornell's Legal Information Institute records the amendment adopted April 29, 2015 and effective December 1, 2015. Under it, "An objection must state whether any responsive materials are being withheld on the basis of that objection." Rule 34(b)(2)(B) adds the item-by-item duty: for each item or category, the response either permits inspection or states its grounds "with specificity," including the reasons.

Read together, the two clauses turn every objection into a factual claim. A fluent objection is not a true one. Each objection becomes a door, and the rule asks what waits behind it. No model learns that by reading the request alone.

The common assumption holds that the difficult part of a discovery response is the language. In truth the language was always the lighter burden, and generative tools now produce it with ease. What no template carries is the inner knowledge of the production: which pages left, which stayed, and on what ground they stayed. That knowledge lives in the review record or it lives nowhere.

Relevant e-Discovery was built around that record. Originals stay immutable with content hashing, audit trails are append-only, chain of custody is documented, and a fail-closed privilege gate sits on production, so the account of what left the building is written while the work happens. The same design joins the defensible spine that enterprise platforms provide (Bates, privilege, chain of custody) with the cited Q&A and chronologies that newer AI entrants offer, because an AI draft is only as honest as the Bates production record beneath it.

Combining 4 sources points to one quiet conclusion: drafting has become cheap, and knowing has not. The gap between the two is where sanctions motions are born.

Yet the simple answer bends the moment you look at how the rule once read, and the bend begins in its older text.

What Breaks When an AI Draft Cannot Point Back to the Exhibit?

The withholding statement becomes a guess. An AI draft is safe to sign only when each sentence links to the exact exhibit behind it, verifiable in one click before filing.

Source-linked output is the structural answer to the sanctions risk that Mata v. Avianca made famous. Yet a link to an exhibit shows what was produced. It cannot show, by itself, what was held back.

The older text of the rule shows where that friction was born. The copy of Rule 34 published by the U.S. District Court for the Northern District of Illinois, as amended through December 1, 1993, required a response "with respect to each item or category" and, on objection, only that "the reasons for the objection shall be stated." No sentence about withholding appears in it. Any objection template drafted under that text inherits its silence.

The same text gives the producing party a choice. Documents may be produced "as they are kept in the usual course of business" or organized and labeled "to correspond with the categories in the request." The rule does not require both.

Here the tension sharpens. A production kept in the usual course arrives as a river of pages without request numbers. One Bates range may answer several requests; another may answer none cleanly. An AI tool handed only the production sees pages, not requests. It can describe what is there with great fluency, and still it cannot say which responsive page stayed home, or under which objection it stayed. A bare relevance tag suffers the same blindness in another key, as the account of where AI relevance scores come from describes.

That silence has a price. Under the same text, the requesting party may move for an order under Rule 37(a) on "any objection to or other failure to respond to the request or any part thereof." A guessed withholding statement invites that motion.

The remedy is not a better prompt. It is a tool that has read the whole collection, not only the outgoing set. Relevant's demonstration asks a firm to bring a messy collection and a hard question, then watch the software read, code, and cite the firm's own documents rather than a case study about someone else's. The deployment can run inside the firm's own AWS account under its own keys, a trust posture no consumer AI tool and almost no small-firm tool can match.

A tool that coded the whole collection holds the remainder as well as the production. That remainder is where the withholding answer lives, in the dark beside the light that was sent.

So the repair begins before the draft, in the words a response uses to fence its own search.

How Should an AI-Drafted Response State What It Withheld?

A review run single-tenant, with no vendor retention and no model training, can hold the record that lets each objection name its search limits and what stayed back.

The rule's drafters already wrote the method down. The Advisory Committee's notes to Rule 34 say that "An objection that states the limits that have controlled the search for responsive and relevant materials qualifies as a statement that the materials have been 'withheld.'" The same notes add a release: the producing party "does not need to provide a detailed description or log of all documents withheld," though it must alert the other side that documents have been withheld.

Contrary to a common reading, then, the rule does not demand an inventory. It demands an honest boundary. A boundary in time counts too: staged productions should name their beginning and end dates.

Practitioners have been slow to absorb this. A Troutman insight on the Sedona Conference's Rule 34 Primer records Magistrate Judge Andrew Peck's warning that "[m]ost lawyers who have not changed their 'form files' violate one or more (and often all three)" of the amendments. The same piece flags the phrase "to the extent that" as almost always a mark of boilerplate, and retires the familiar subject-to-and-without-waiving formula. When a party will produce less than was asked, it can say so directly, or indirectly by describing the custodians, date ranges, and search terms it will use.

I would strike every "to the extent that" from the form file before any model learns to imitate it. A model trained on old habits returns old habits, only faster.

Here is the difference in one objection, shown as an illustration with placeholders rather than figures from any matter:

• Before: Responding party objects to this Request to the extent that it is overbroad. Subject to and without waiving these objections, responding party will produce responsive, non-privileged documents.
• After: Responding party objects to this Request as overbroad in time. Its search was limited to [custodians], [date range], and [search terms]. Responsive documents outside those limits are withheld on the basis of this objection. Documents within them are produced at [Bates range].

An AI tool can write the second version only if the bracketed values exist somewhere it can read. Fill those brackets during review. Then ask whether opposing counsel could check each one.

Where that record lives matters as much as whether it exists. When the review stays inside the firm's control, nothing fed to the tool trains anyone's model or waives privilege. Relevant was built for the solo or small firm facing one messy matter, the segment Relativity and Everlaw price out and cannot serve without an administrator; the comparison of per-matter and annual platform pricing shows how that economics plays out for a firm of that size.

What remains is to give the boundary a shape, one request at a time, with Bates numbers on either side of the line.

What Should a Firm Do Before Its Next AI-Drafted Response?

Build the Request-to-Bates Map during review, then let AI draft from it; the withholding sentence becomes a fact you can show rather than a phrase you hope is true.

A HaystackID panel, in a webcast transcript published by JD Supra, compressed defensible AI into one instruction: "have the policies, have the process, have the proof." The same panel warned that some provider agreements retain data "potentially for logs, uh, abuse review," and that retained data stays potentially discoverable.

Two things follow. The map is the process. The retention terms decide whether your drafting history joins the production one day.

I expect the withholding sentence to become the first line opposing counsel tests in any AI-assisted response, because it is the one line a fluent model can get wrong while sounding entirely right. Speed was never the KEY. An early case assessment screen will not carry you through that night either: one panelist called such a screen "gonna be over inclusive" and unfit to produce from.

Start with the next request on your desk, and the Bates range that answers it.