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Targeted discovery requests: tie every request to an element you must prove

You are drafting the first set of discovery requests and the template is fifty generic items. Build requests tied to the elements and facts that matter in this case.

The prompt — copy and run it

You are a litigation associate drafting a discovery plan for a licensed attorney's review — work product and a drafting aid, not legal advice. I will paste the claims and defenses in general terms and the anonymized key facts.

Produce:

A) ELEMENT MAP — each claim and defense broken into elements, and for each element the facts we need and whether we have them.

B) REQUEST DRAFTS — document requests, interrogatories, and admission requests aimed at the gaps, each tagged with the element it serves. Keep each request specific in time range and subject.

C) PROPORTIONALITY NOTES — requests likely to draw a burden objection, and how to narrow them in advance.

D) SEQUENCING — what to serve first so the answers shape later requests and deposition topics.

E) RULE CHECKS — numerical limits, timing, and format requirements to verify in the applicable rules and local rules.

Inputs: [CLAIMS AND DEFENSES IN GENERAL TERMS] · [ANONYMIZED KEY FACTS + KNOWN GAPS] · [COURT + JURISDICTION] · [ANY SCHEDULING ORDER LIMITS]

Rules: Anonymize before you paste and keep privileged strategy and protected personal data out of a consumer LLM (ABA Formal Op. 512). Do not invent facts, rules, or numerical limits — every rule point is flagged for the attorney to verify in the governing and local rules. Verify each request against the scheduling order and any protective order before service. The attorney owns every request served.
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How to use this prompt

  1. Copy the full prompt above with the Copy button.
  2. Fill in your inputs. Replace each bracketed placeholder with your specifics: [CLAIMS AND DEFENSES IN GENERAL TERMS][ANONYMIZED KEY FACTS + KNOWN GAPS][COURT + JURISDICTION][ANY SCHEDULING ORDER LIMITS]
  3. Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.

Why this prompt works

Generic discovery templates produce generic answers and burden objections. Mapping requests to the elements that must be proved focuses discovery on the facts that decide the case, and anticipating proportionality objections saves a round of meet-and-confer. Sequencing turns early answers into better later requests and sharper deposition outlines.

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Reality guardrail: this prompt makes the model reason from data you paste — it does not source or verify facts for you. Check every claim, keep confidential data out of consumer AI tools, and follow your employer's AI-use policy.

Frequently asked

When should I use this prompt?

You are drafting the first set of discovery requests and the template is fifty generic items. Build requests tied to the elements and facts that matter in this case.

Why does this prompt work?

Generic discovery templates produce generic answers and burden objections. Mapping requests to the elements that must be proved focuses discovery on the facts that decide the case, and anticipating proportionality objections saves a round of meet-and-confer. Sequencing turns early answers into better later requests and sharper deposition outlines.

What mistake does this prompt help you avoid?

Fifty-item generic discovery sets: every request is tied to an element, narrowed for proportionality, and sequenced so early answers shape later ones.

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