Clio’s “ChatGPT Prompts for Lawyers” page, updated August 2026, opens with: “Conduct legal research on [legal issue or topic]. Please be concise. Summarize the relevant case law, statutes, and regulations.” Not the tenth prompt. The first. No jurisdiction, no materials, a general model asked to produce authorities from memory: almost word for word the instruction that has put lawyers in front of irritated judges since Mata v. Avianca. The same page dates ChatGPT’s knowledge to 2021 and, three paragraphs later, to 2023 (Clio).
By 12 September 2026, Damien Charlotin’s database had logged 2,039 court decisions involving hallucinated material, 811 involving lawyers. The database classes 1,689 of them as fabrications: the signature output of a general model asked to supply law from memory.
So each prompt here comes with the output to expect, what goes wrong, and what to check. They work in ChatGPT, Claude, Gemini and Copilot alike.
How to use this library (and the one prompt never to use)
Tier before text. Client material goes only into a tier that does not train on it (a business or enterprise tier, or a legal platform). In United States v. Heppner (S.D.N.Y., February 2026) Judge Rakoff held a defendant’s exchanges with consumer Claude neither privileged nor work product: “Because Claude is not an attorney, that alone disposes of Heppner’s claim of privilege.” Anonymise, do not merely redact: [PARTY_A], [AMOUNT_1], key offline. Give the model an exit (“NOT IN DOCUMENT”, “[VERIFY]”) or it fills the gap.
WORKING RULES
1. Jurisdiction: [jurisdiction]. Apply only [jurisdiction] law.
2. Use only the materials I provide. Tag any authority from your own knowledge [VERIFY]; I will check it in a primary database.
3. If you cannot support a proposition from the materials or binding [jurisdiction] authority, write "NO VERIFIABLE AUTHORITY FOUND".
4. Never invent facts, dates, amounts, names or quotations. Leave a [BRACKET] instead.
5. Label facts, inferences and assumptions separately.
6. If the request is ambiguous, ask up to three questions first.
7. Everything you produce is a draft for review by a licensed lawyer.Research prompts: the negative-constraint pattern
Justia gives research prompts an explicit refusal path: “NEGATIVE CONSTRAINT: If a specific legal proposition or rule cannot be verified by a direct citation to official, binding authority within [Jurisdiction] … explicitly state ‘NO VERIFIABLE LOCAL AUTHORITY FOUND’ for that point” (Justia Onward). Even so, keep general models away from the citation step: Stanford’s 2024 benchmark put hallucination at more than 17% for Lexis+ AI and more than 34% for Westlaw AI-Assisted Research. Use ChatGPT or Claude for structure, a grounded platform and a citator for authorities (AI legal research without hallucinations).
[Working rules]
Build the analytical skeleton for a memo on whether [client, anonymised] can [establish / defend] a claim for [cause of action] under [jurisdiction] law on these facts: [facts].
Output: (1) the elements, numbered, with the standard of proof; (2) per element, the facts that support it, cut against it, and are still unknown; (3) the three most likely defences and what must be true for each; (4) the search queries I should run in [Westlaw / Lexis / BAILII / juris].
Do not cite cases. Cite statutes only where confident, tagged [VERIFY].Expect: an element list and usable search strings. What goes wrong: mixed jurisdictions; check the elements in a practitioner text.
| # | Prompt sketch | Expect, then check |
|---|---|---|
| 3 | Opposing counsel against [position]: three counter-arguments by danger, the judge’s question I least want, what you would concede. | A critique; if soft, “You conceded too easily” |
| 4 | Rules and provisions governing [step] in [court], from [official sites] only, quoted with time limit and consequence. | A checklist; verify each provision at source |
| 5 | List every premise in “[question]”; mark each established, contestable or unverifiable; re-state the question if one is wrong. | Your assumptions exposed; models accept false premises (Stanford’s Ginsburg-dissent test) |
| 6 | Against my memo dated [date], using only [named sources]: what changed, when, source, paragraph; otherwise NO CHANGE FOUND. | A change log; run the citator anyway |
| 7 | (Deep Research) Briefing on [regulatory area] as at [date]: instruments with official links, dated timeline, open questions. | An overview; click every source |
Contract review prompts: table output with clause references
In the “Better Call GPT” study a GPT-4-class model matched junior lawyers at determining whether a clause raised an issue (F-score 0.871 v 0.860) but was weaker at locating it (0.686 v 0.770 for outsourced reviewers). So every review prompt must force quoted words and a clause reference (AI contract review against a playbook).
You are an experienced [jurisdiction] in-house commercial lawyer. We (the customer) have received the attached [vendor agreement] from [Vendor].
Prepare a table, one row per section, four columns: (a) why the section is good or bad for us, quoting the operative words; (b) how you would change the wording in our favour and justify the edit to the vendor's lawyers; (c) the arguments the vendor's lawyers will make against the change; (d) how you would respond.
Each cell under 60 words. Mark the five rows where you expect most resistance. Start with the 15 riskiest sections.Miller’s original is on Ten Things.
| # | Prompt sketch | Expect, then check |
|---|---|---|
| 9 | Harvey’s template: review “from the perspective of a US-based enterprise customer” with “a table with clause reference, issue, business impact, suggested revision, and priority level”. | Issues table; read every “high” row |
| 10 | Read title, recitals, exhibit titles and signature blocks first; state agreement type, which party I represent, governing law, missing exhibits. Then stop. | A routing note; confirm the side |
| 11 | Review this NDA against [playbook], our side: per item, clause quoted / GREEN, YELLOW or RED / impact / redline / escalation. | Traffic-light table; read every RED and YELLOW |
| 12 | Second pass, quoting the words: non-renewal notice conditions; symmetry of caps; conflated damages categories; the definition chain behind [Confidential Information]. | Blind-spot list; trace the definition chain yourself (Farrell Fritz: “three layers deep”) |
| 13 | Compare [our_draft] with [their_markup]: every change including deletions, effect, severity (Dealbreaker to Cosmetic), response. | Change table; run Word compare too, “Cosmetic” defined-term tweaks are often substantive |
| 14 | One-page commercial summary for [the CFO] with clause numbers; then the three obligations we are most likely to breach by accident. | One page; summaries flatten conditions |
Contract drafting prompts: clause-level building blocks
Miller’s clause prompt works because it is narrow: “Draft a limitation of liability clause for a SaaS agreement, capping liability at 12 months of fees paid and excluding indirect damages.” Never ask for a whole agreement: whole-SPA prompts produce undefined terms, broken cross-references and warranties a seller “can’t possibly know”. And “AI tools are also often geared towards a US audience” (Darwin Gray).
| # | Prompt sketch | Expect, then check |
|---|---|---|
| 15 | Three versions of Miller’s SaaS liability cap for the [supplier’s] side under [jurisdiction] law: CLIENT-FAVOURABLE, MARKET-STANDARD, AGGRESSIVE, each with assumptions and the likely counter-argument. | Three clauses; check “market” against your own deals |
| 16 | IP indemnity for the licensor from house-style samples [sample_1] to [sample_3]; then the three substantive differences and why. | A clause in your voice; every defined term resolves |
| 17 | From [term_sheet] and [precedent], draft ONLY the [price adjustment] block; mark [CONFIRM: term sheet silent] and [TERM SHEET OVERRIDES]. | One flagged block; cross-references, repeat per block |
| 18 | Written resolutions of the [board] approving [transaction] from [precedent] and [articles]; confirm authority, quorum and majority, citing articles, marked [CONFIRM]. | Resolutions; check quorum yourself, never transcribe board minutes with AI |
| 19 | Rewrite [text] for a lay reader without changing its legal effect; then a two-column table original / rewritten. | Plain English plus diff; models soften “must” to “should” |
Litigation prompts: depositions, cross-examination, authority checks
Clio’s deposition prompt is widely copied: “Summarize this 80-page deposition of the plaintiff’s HR manager. Identify statements that contradict the verified complaint … Output as a table with page and line citations.” The page-and-line instruction is the good part; but Clio itself warns that a prompt stacking summary, credibility, comparison and follow-up “does none of them particularly well”. Separate extraction from judgement.
[Working rules]
Summarise the attached deposition of [the HR manager, "DW3"]: (1) admissions relevant to our summary-judgment motion on [issue], each with page:line; (2) statements that contradict the verified complaint <complaint>[paste]</complaint>, as a table: complaint paragraph / complaint statement / deposition page:line / deposition statement / inconsistency; (3) internal inconsistencies; (4) topics the witness could not recall.
Quote, do not paraphrase. Do not assess credibility or suggest follow-up questions yet.| # | Prompt sketch | Expect, then check |
|---|---|---|
| 21 | Harvey’s: from the expert report, transcript and chronology, “draft targeted cross-examination questions for the opposing expert” and “include the source passage that supports each line of questioning”. Add: leading form; no passage, no question. | A sourced outline; read every passage (deposition prep) |
| 22 | Harvey’s: “Review this draft brief section. Identify unsupported propositions, missing authority, overstatements, and arguments that need stronger factual support.” | A to-do list; logic only, citations still go through a database |
| 23 | Per document: date, author, recipients, neutral summary, mentions [issue]? (quote), admission or promise (quote); sort by date; DATE UNCERTAIN where content conflicts. | A chronology; check every “admission” cell, OCR first |
| 24 | Act exclusively as a sceptical appellate judge who has read both briefs: one hard question at a time, follow up if I evade, until “Time Out”. | A moot; client consent first, any case cited is [VERIFY] |
| 25 | Every authority in the opposing brief: citation as written, proposition quoted, pinpoint given?, red flags, priority. Do not tell me whether it exists. | A cite-check worklist; in Noland the winners lost their fees for not flagging fakes |
| 26 | This pro se filing appears AI-assisted: relief and theories in 150 words; every authority with its proposition; a neutral paragraph for the court on citations we could not locate. | Triage note; verify before telling a court a case does not exist |
In-house prompts: playbooks, DPAs, litigation holds
Miller’s “Draft a litigation hold notice for employees following the filing of a lawsuit against the company” is a starting line: the model does not know your systems, so name them.
| # | Prompt sketch | Expect, then check |
|---|---|---|
| 27 | Litigation hold for [Company] about [subject, anonymised]: why it applies; data categories incl. [named systems]; date range; suspend auto-deletion; five employee FAQs. | A notice; scope against the claim and your system list |
| 28 | From five signed [NDAs], extract the position accepted per clause type, then Preferred / Fallback / Walk-away with model clause and escalation trigger. | A playbook; a GC signs every line (LegalOn: 34% of teams have none) |
| 29 | Triage this NDA against [standard] and [rules]: STANDARD APPROVAL / COUNSEL REVIEW / FULL REVIEW; rule triggered, clause quoted; two-sentence reply. | A routing decision; review every STANDARD APPROVAL for the first 30 |
| 30 | Review this DPA against [standard] and GDPR Art. 28(3): each element present / missing / weaker, clause quoted; five push-back points. | A compliance matrix; MISSING items against the annexes |
| 31 | Review this invoice against [ocg]: block billing, vague entries, rate overages, duplicates, AI-assisted tasks billed at pre-AI durations; table plus courteous email. | Adjustments; models miscount hours, total in Excel |
| 32 | Summarise this outside counsel memo for [the VP Sales]: decision required, recommendation, three risks, next steps, every qualification under “Conditions”. | One screen; wide forwarding can waive privilege |
Client communication prompts: plain English, tone, length
The lowest-risk family; Clio’s “explain the discovery timeline … in plain English … reassuring but honest about potential delays … under 200 words” is close to ideal (prompt 34). The harder case is the client who arrives with a chatbot’s answer.
A client has sent me this AI-generated analysis <client_ai_text>[paste]</client_ai_text>, which conflicts with my advice <my_advice>[paste]</my_advice> under [jurisdiction] law.
Draft a reply that: thanks them; identifies precisely where the AI text goes wrong (wrong jurisdiction, outdated law, invented authority, missing fact), one sentence each; explains in plain terms why our advice stands; and warns, in one sentence and without lecturing, that pasting our communications into public AI tools can jeopardise confidentiality and privilege.
Warm, brief, no defensiveness. Under 200 words.Check first: whether your own advice is still right. The privilege warning is real: the Upper Tribunal held that putting client letters into ChatGPT “is to place this information on the internet in the public domain” ([2026] UKUT 81 (IAC); more in AI for client communication).
| # | Prompt sketch | Expect, then check |
|---|---|---|
| 34 | Client email on [the discovery timeline], under 200 words: what happened; what it means for you; what we are doing; what we need by [date]; no promised outcome. | A structured email; check dates, read it as the client |
| 35 | Email reporting [the court denied our motion]: fact in sentence one; three options with cost, timing and likelihood from [options]; recommendation; then five bullets for a call. | Bad news front-loaded; the model softens “denied” |
| 36 | Engagement-letter clause on our AI use meeting an informed-consent standard: tools and tiers, what is processed, specific risks, human review, right to object, actual time only. | A readable clause; ABA Formal Opinion 512 says boilerplate “is not sufficient” |
Business development prompts: bios, LinkedIn, meeting prep
Content Pilot’s fifth bio prompt is the test most bios fail: “Do the first 2-3 sentences describe the lawyer’s practice and the type, size, location, and industry sector of clients represented?”
| # | Prompt sketch | Expect, then check |
|---|---|---|
| 37 | Audit this bio: do the first sentences describe the practice and the type, size, location and sector of clients? Client-focused, active voice, scannable? Rewrite; table every credential kept. | A rewrite; check every credential, Rule 7.1 has no “AI drafted it” defence |
| 38 | Turn this client alert into a 150-220-word LinkedIn post in my voice from three past posts: practical consequence first; one concrete example; no hashtags; one genuine question. | A draft you still edit; 81.2% of July 2026 posts rated “Likely AI” (Originality.AI); see LinkedIn with AI |
| 39 | (Deep Research) One-page brief on [name], [title], [company] from public sources: role, six months’ news with links, three legal pressures, three openers, one thing not to raise. | A cited one-pager; click the links |
German-language prompts: Gutachtenstil and Normenrecherche
BRAK’s frame: where possible only “abstrakte” prompts go into language models, prompts “die auch im Kontext keinerlei Rückschlüsse auf ein bestimmtes Mandat zulassen”. Lulius’s rule: “Allgemein-LLM für Sprache, spezialisierte Legal AI für Recht” (Lulius).
Rolle: Volljurist mit Schwerpunkt [Rechtsgebiet]. Sachverhalt (anonymisiert): [Sachverhalt].
Aufgabe 1: Identifiziere die einschlägigen Normen. Format: nummerierte Liste; je Norm: § mit Gesetz, Wortlaut-Kern (max. 2 Zeilen), Relevanz. Markiere jede Norm mit [PRÜFEN].
Aufgabe 2: Formuliere den Obersatz nach dem Schema "A könnte gegen B einen Anspruch auf [Rechtsfolge] aus § [Norm] haben."
Aufgabe 3: Liste die Tatbestandsmerkmale in Prüfungsreihenfolge und nenne zu jedem die fehlende Sachverhaltsangabe.
Keine Rechtsprechung zitieren. Bei Unsicherheit zum Normtext: "WORTLAUT PRÜFEN".What goes wrong: Fundstellen built from real fragments. The Kammergericht Berlin documented a fake “BGH, Beschl. v. 14.11.2007 – XII ZB 183/07, FamRZ 2008, 137” combining a genuine journal page range with a wrong file number (17 WF 144/25). Check every § and every decision in juris, beck-online or RIS.
Building your own library and logging what worked
Forty prompts on a web page are not a firm’s library; three habits make one.
Log every run. The NC Bar’s 2024 advice: a spreadsheet of which model you used, the prompt, the output and whether it succeeded (NC Bar).
Build it as a team. Sterling Miller: “One thing I highly recommend is for legal departments to work as a team to develop their own library of standard prompts tailored to the specific needs of the department and the company.” Each entry needs an owner, the approved tier, the verification steps and a “last tested” date with the model version, because “a model will update and I will need to tweak the prompt again” (r/biglaw).
Stop re-typing the context. Anything true across matters belongs in a reusable workspace: Claude Projects and custom GPTs for law firms.
Where to go next: the frameworks are in prompt engineering for lawyers and the prompting hub; the verification protocol is in how to verify AI legal citations; the searchable collection is the prompt library. In AI Lab for Lawyers we run these prompts live on anonymised documents; participants leave with more than 300 slides of tested prompts and the habit of checking outputs that this page models.
Frequently asked questions
What are the best ChatGPT prompts for lawyers?
The best ChatGPT prompts for lawyers share five features: they state whose side you are on, name the jurisdiction, supply the materials the model must work from, specify the output (usually a table with clause or page references) and give the model a permitted way to say it does not know. Prompts that ask a general model to supply case law from memory are the worst, because that is where fabricated citations come from.
Can I use the same prompts in Claude and Copilot?
Yes. Every prompt on this page works in ChatGPT, Claude, Gemini and Microsoft Copilot with at most cosmetic changes. Two adjustments matter: on reasoning models such as OpenAI's o-series or Claude Opus 5, drop 'think step by step' and 'double-check your answer', because the vendors say those lines are unnecessary or cause over-verification; and on Copilot, name the file you want it to read, since it lives inside Word or Outlook.
Is it safe to paste client facts into a prompt?
Only on a tier that does not train on your data and only after anonymising. ChatGPT Plus and Claude Pro train on conversations by default; ChatGPT Business and Enterprise, Claude Team and Enterprise and Copilot with enterprise data protection do not. A federal court held in 2026 that a defendant's exchanges with consumer Claude were not privileged. Replace names, amounts and identifiers with placeholders and keep the key offline.
What is a good prompt for contract review?
State the side you act for, the governing law and the commercial posture, then ask for a table with fixed columns: clause reference, quoted operative words, issue, business impact, proposed revision and priority. Add 'write NOT PRESENT where a term is absent' so the model cannot invent a renewal term or cap. Then run a second-pass prompt for the known blind spots: asymmetric caps, auto-renewal notice conditions and conflated damages categories.
How do I build a prompt library for my firm?
Start with the five tasks your team repeats most. For each, save the prompt with placeholders, the approved tool and tier, what must be anonymised, the expected output, the mandatory verification steps and a 'last tested' date with the model version. The NC Bar recommends logging model, prompt, output and success in a spreadsheet; Sterling Miller recommends building the library as a team. Re-test every quarter, because model updates change what a prompt produces.