How to Draft Discovery Requests and Responses You Can Trust

A litigation associate opens a matter, feeds the pleadings to an AI tool, and generates a full set of interrogatories and requests for production in minutes. The draft reads clean. It tracks the elements of every claim and defense, the numbering is tidy, and the definitions look right. By the old clock, a week of work is done before lunch.

Then the supervising partner asks the question that decides whether any of it gets served, whether every request is warranted by this record and proportional to this case, because the signature certifies exactly that. AI has made the first draft of discovery nearly free. The work that remains, the work that was always the most complex, is grounding each request in the facts of the matter and verifying it before it goes out.

A discovery set copied from generic templates costs you in two ways. Overbroad requests draw objections, then meet-and-confer letters, then motion practice, and in the worst cases a fight over sanctions under Rule 26(g). A set that misses the record cuts the other way, leaving evidence unrequested and a case underbuilt. Throughout the rest of this article, we walk through how to draft discovery requests that are grounded, proportional, and verifiable enough to sign, and where AI grounded in the case record helps you get there.

Drafting Discovery Requests in Civil Litigation

Drafting discovery requests means writing the formal instruments litigants use to obtain evidence, mainly interrogatories, requests for production, and requests for admission. Strong requests tie each question to a claim or defense element, ground their scope in the case record, and stay proportional under the rules so they survive objections.

The three instruments do different jobs. Interrogatories under Rule 33 ask the opposing party to answer questions in writing and under oath. Requests for production under Rule 34 call for documents, electronically stored information, and tangible things. Requests for admission under Rule 36 ask a party to admit or deny specific facts, which narrows what anyone has to prove at trial.

Most guidance stops at that taxonomy, and most AI tools stop at speed. Neither addresses the problem a litigator actually has. The draft is easy to produce. Making it right, and being willing to stand behind it, is the job. Every request you serve carries your certification under Rule 26(g). Speed is the easy part, and the real question is whether the set is grounded in the matter and tight enough that you can put your name on it. The rest of this piece treats drafting as that grounding and verification problem.

Verifying AI Discovery Requests Before You Sign

What makes an AI-drafted set worth serving is whether it is grounded and verifiable enough that a lawyer will sign it. Rule 26(g) makes that the operative test. When you sign a discovery request, you certify something specific to the court. To the best of your knowledge after a reasonable inquiry, the request is warranted by existing law or a good-faith argument to change it. The request is not interposed for an improper purpose, and its scope is neither unreasonable nor unduly burdensome given the needs of the case. A reasonable inquiry is the part AI cannot perform for you.

That certification turns verification into a defined task. Before you sign an AI-drafted set, work through a short checklist.

• Does each request map to a specific claim or defense element you have to prove or rebut?
• Is each request grounded in this matter's own record and specific to this case?
• Is the scope proportional under Rule 26(b)(1) given the amount in controversy and the needs of the case?
• Is the phrasing precise enough to prevent evasive or partial answers?
• Are the definitions, instructions, and instrument choices consistent with the governing rules and your venue's local rules?

The design of the AI tool you use matters here. For example, Harvey grounds each answer in sources your team trusts and drafts inside Word through its ecosystem. A litigator can trace a given request back to the document or rule behind it and check it before signing. Grounding and traceability are what let verification happen quickly.

None of that moves the signature off the lawyer's desk. The AI Fluency and the Future of Associate Development at Law Firms report examines that responsibility directly, mapping how firms are rebuilding training and supervision for an AI-heavy workflow. Reviewing, verifying, and pressure-testing AI output remains the work of the lawyer, and a firm should set clear expectations for it. Treat every AI-generated draft as a starting point. A qualified lawyer must review AI-generated output and confirm it against the record and the governing rules before relying on it or serving it.

Grounding Discovery Requests in the Case Record

A request grounded in the matter is narrower and harder to fight than one pulled from a form bank. Grounding starts with reading: close reading of the complaint, the answer, and the initial disclosures. The complaint tells you which facts you have to prove. The answer tells you which of those facts are actually in dispute, so you stop spending requests on admitted ground. The disclosures name the people and categories of documents the other side already concedes are relevant, which is the first map of where the evidence sits.

From there, the documents already in the matter do the rest of the work. The contracts, correspondence, and records you hold let you name real custodians, real date ranges, and real document types in a request. Asking for everything invites an objection. A request that names the three custodians and the eighteen-month window that matter is precise, proportional, and difficult to refuse.

Your organization's own prior discovery is the third source. Definitions and request sets your litigators have already served and defended carry tested language, and reusing that language keeps a new set consistent and harder to pick apart. AI can accelerate this work when it is grounded in the matter. With Harvey Vault, litigators can analyze the full document set in a matter to surface the facts, gaps, and custodians that should shape a request. Harvey's agents inherit the project's context and your team's memory, so a drafted set reflects this specific case and the way your team works.

The payoff is not theoretical. According to The Accelerating Impact of Legal AI, litigation is among the most common areas where firms deploy Harvey's AI agents. The report describes a litigation partner who dropped a case file into Harvey and, working with the team, surfaced factual holes in the opposing case that moved it to settle within a week. Grounding is what turns a fast draft into that kind of result.

Drafting Interrogatories and Requests for Production With AI

Each instrument rewards a different kind of care, and grounded AI can produce the first draft of each while you make the decisions that matter.

Interrogatories work best when they are specific and few. Contention interrogatories ask the other side to state the facts behind a claim or defense, which locks in their position early. Identity interrogatories ask who did what and who knew it, which builds your witness list. Rule 33 caps you at 25 interrogatories including discrete subparts absent a stipulation or court order, so every question has to earn its place. A compound interrogatory that buries three questions in one wastes the count and hands the other side room to answer narrowly.

Requests for production carry the heaviest drafting load, because scope is where they live or die. A sound request defines the documents and electronically stored information it seeks, names custodians and date ranges, and ties its reach to the claims in the case. Rule 34 requires reasonable particularity, and a request that describes categories with real specificity is far harder to resist than a sweeping demand. Requests for admission under Rule 36 do quieter work, retiring facts and authenticating documents so the trial narrows to what is genuinely contested.

Across all three, the failure modes are the same: compound requests, overbroad scope, and vague phrasing that invites an evasive answer. Grounded AI helps by getting the legal drafting right so your attention goes to strategy. For example, Harvey drafts a first set grounded in the matter and the governing rules. Harvey’s knowledge sources pull in the controlling authority and local rules that shape definitions, instrument choice, and numerical limits, so the litigator starts from a targeted draft and edits for theory of the case. The draft is a starting point, and a lawyer verifies every request against the record and the rules before it is served.

Proportional Discovery Requests and Fewer Objections

Proportionality is usually discussed as something the responding party raises in the legal discovery process. It is more useful as a rule you draft to. Rule 26(b)(1) limits discovery to what is proportional to the needs of the case, and it names six factors to weigh. The first three are the importance of the issues, the amount in controversy, and the parties' relative access to information. The rest are the parties' resources, the importance of the discovery to resolving the issues, and whether the burden outweighs the likely benefit. A request written with those factors in mind is a request that is hard to object to.

The alternative writes itself into a cycle. Boilerplate requests draw boilerplate objections, which produce a meet-and-confer letter, which produces a motion to compel or for a protective order, which produces delay and cost before a single document changes hands. Every overbroad request you serve is an opening for that cycle. Right-sizing scope up front, by naming custodians and windows and tying each request to a live issue, closes most of those openings before they exist.

The discipline worth building is red-teaming your own set before it goes out. Read each request as opposing counsel would and ask where you would object, on breadth, on burden, on relevance, and then tighten or cut. A litigator can use Harvey to run that pass at speed, analyzing a drafted set for overbroad or compound requests and checking its scope against the proportionality factors. This way, the weak spots surface on your side of the table before opposing counsel raises them.

What Grounded Discovery Drafting Returns

The return on grounded AI in discovery drafting shows up in two places, the hours a team gets back and the quality of the requests that go out. Recovered hours are one of the clearest measures of law firm productivity.

The capacity gain is real and measurable. The Adecco Group selected Harvey after testing multiple generative AI tools and prioritizing legal precision. Its legal team reports up to eight hours saved per lawyer each week and a 5-10% cut in outside counsel spend, with Harvey embedded in workflows from contract review to litigation modeling. That is the capacity a litigation team can redirect from mechanical drafting to the reading, strategy, and verification that decide cases.

Quality is the other half, and it starts at selection. Repsol chose Harvey after a blind test showed it produced the highest-quality output across a range of legal tasks. Adoption reached 96% across the legal department, with Harvey embedded across work that includes litigation preparation. A quality-first choice is the right frame for discovery drafting, because the whole argument here rests on output a litigator can verify and trust enough to sign.

Neither result, though, means much on its own. Capacity matters only when the work that comes out is grounded and verifiable enough to put your name on. The hours are the means. A tighter, better-grounded set that a partner will sign is the end.

Drafting Discovery Requests You Can Sign

Agentic drafting is making the first set of discovery nearly free, and that changes where a litigator's edge sits. The scarce work is now the grounding and verification that confirm a request is warranted by this record and proportional to this case, which is where matters are won or lost.

That work belongs to the lawyer, who reads the set, pressure-tests it, and signs it under Rule 26(g). The measure of any AI-drafted set is the same as it has always been for a junior associate's draft, whether it is grounded, traceable, and verifiable enough to sign. Treat the draft as a first pass. A qualified lawyer must review AI-generated output against the record and the governing rules before relying on it or serving it.

This is the case for grounded legal AI in discovery drafting. Harvey grounds each request in the matter's own record and your team's prior sets, and drafts inside Word where litigators already work. Harvey researches the controlling authority and local rules that shape definitions and limits, and it checks a drafted set of requests against the proportionality factors before you serve. The first draft starts closer to signable, and your time moves to strategy and verification. See how Harvey drafts and grounds discovery you can sign in a live demo.

Frequently Asked Questions

Can AI draft discovery requests?

Yes. Legal drafting AI can generate a first set of interrogatories, requests for production, and requests for admission from the pleadings and the case record in minutes, and it handles the mechanical draft well. A lawyer still has to ground each request in the matter, check it for proportionality, and verify it before signing and serving it.

What are the risks of using AI to draft discovery requests?

The main risks are ungrounded requests that miss the record, overbroad scope that draws objections and motion practice, and misplaced trust in a clean-looking draft. Rule 26(g) puts the certification on the signing lawyer, so an unverified AI draft that goes out unchecked exposes you to sanctions and a weaker case.

How do you keep AI-drafted requests from drawing objections?

Ground each request in the case record and draft to proportionality. Name real custodians, date ranges, and document types, tie every request to a live claim or defense, and keep the scope within the Rule 26(b)(1) factors. Then red-team the set for breadth and burden before serving, and tighten anything opposing counsel could challenge.

Does an attorney still have to sign an AI-drafted discovery?

Yes. Rule 26(g) requires an attorney of record to sign every discovery request, and that signature certifies a reasonable inquiry and that the request is warranted, proportional, and free of improper purpose. AI cannot make that certification. The duty of competence under ABA Model Rule 1.1 Comment 8 reinforces that the lawyer owns the review.

What is the difference between interrogatories and requests for production?

Interrogatories under Rule 33 are written questions the opposing party answers in writing and under oath, limited to 25 absent agreement or a court order. Requests for production under Rule 34 seek documents, electronically stored information, and tangible things from a party. One asks for answers, the other asks for materials, and the collection of those materials is the domain of ediscovery for law firms.