AI by Practice Area: Tasks, Traps and a Starter Prompt for Each Type of Lawyer

A divorce lawyer on r/Lawyertalk says AI “cranks out standard petition and motions easily”, with “no need for a ton of legal research or cites”. A patent attorney reading the USPTO’s April 2024 guidance learns that running a draft through a chatbot hosted on a server abroad may breach a foreign-filing licence. Same technology; neither would recognise the other’s advice as relevant.

That is the problem with most “AI for lawyers” content: it is written for a lawyer who does not exist. AI by practice area is the only frame that survives contact with a real desk, because three things change with your field: which tasks the model does well, which confidentiality rules bite, and how, precisely, people like you have been sanctioned.

The cards below use one evidence standard. Each gives three tasks with a source, three traps, one verified sanction story and one prompt you can paste today. Read the pattern section first, then your card, then the full guide in the practice-area cluster.

The pattern: volume practices get sanctioned, sophisticated practices get embarrassed

Damien Charlotin’s AI Hallucination Cases database logged 2,039 court decisions by 12 September 2026, 811 of them involving lawyers. The top subject areas are not exotic:

Look at who gets sanctioned and a second pattern appears. Lnu v. Blanche (9th Cir., 3 June 2026) was an immigration appeal whose briefs were drafted by unlicensed law graduates; Dehghani v. Castro (D.N.M., 2 April 2025) was an immigration brief bought from a freelancer; In re Martin (Bankr. N.D. Ill., 18 July 2025) was consumer bankruptcy, with counsel admitting “I ran it through AI to some extent”; the Morgan & Morgan sanction in Wadsworth v. Walmart was personal injury. High volume, thin margins, outsourced or junior drafting, and nobody read the filing.

Large-firm failures look different. Latham’s associate asked Claude to format a citation in Concord Music v. Anthropic and got the wrong author and title; a Stanford professor’s “[cite]” placeholder became fabricated studies in Kohls v. Ellison; a judge’s clerk used Perplexity and a judge’s intern used ChatGPT. Sophisticated practices are more often embarrassed than fined, though not always: Ellis George and K&L Gates paid $31,100 in Lacey v. State Farm (C.D. Cal., 6 May 2025) for an outline made with CoCounsel, Westlaw Precision and Gemini that went into a brief unchecked. The mechanism is identical. In one 33-day Westlaw sweep (30 June to 1 August), Thomson Reuters counted 22 sanction cases spanning family law, bankruptcy, immigration, education and civil litigation.

“But a competent and diligent attorney must do more than prompt generative AI, check that the citations provided by the AI are real and the subject matter roughly on point, and call it a day. … A competent and diligent attorney must also read and reason.” — Ninth Circuit, Lnu v. Blanche, No. 24-4790 (3 June 2026)

In-house counsel: playbooks, DPAs and the intake front door

Three tasks that work. NDA triage at volume, “the kind of review a lean team runs a hundred times a quarter” in the words of Snyk’s Alexis Palmer; data-processing-agreement review, which GC AI calls “some of the most repeatable work an in-house privacy team does”; and a Slack or Teams intake front door that sorts requests before a lawyer sees them.

Three traps. No playbook: LegalOn found 34% of teams have none and only 5% a comprehensive one, so “review against our playbook” first means writing it. Copilot oversharing: “Permissions set years ago and never revisited now define what an AI tool will surface on demand” (ABA Law Technology Today). And unprovable value: Axiom’s survey of 528 leaders found only 7% had scaled AI beyond pilots and 83% could not show whether last year’s spend paid off.

The story. In Fortis Advisors v. Krafton (Del. Ch., March 2026) the acquirer’s CEO’s ChatGPT conversations about avoiding a $250m earn-out became evidence; Skadden now cites the case when warning boards about AI-drafted minutes. Your executives’ chats are discoverable. Say so in the AI policy.

You are reviewing an inbound NDA for [Company], which is usually the [receiving/disclosing] party. Compare it clause by clause with our standards guide below and sort every deviation into GREEN (accept), YELLOW (accept with the fallback in the guide) or RED (escalate to counsel). For each YELLOW or RED item quote the counterparty's words, name the guide position and propose replacement wording. Do not add legal requirements that are not in the guide. End with one line: standard approval, counsel review or full review.

Standards guide:
[paste]

NDA:
[paste]

Full guide: AI for in-house counsel.

Employment: handbooks, severance and 409A

Three tasks that work. Thomson Reuters describes three CoCounsel workflows that map onto most employment practices: an executive-agreement review for IRC § 409A (severance trigger dates, the six-month delay for specified employees, acceleration clauses); a handbook compliance audit that outputs a table headed “Topic / Coverage / Section / Recommendations”; and a survey of federal and state wage-and-hour rules for a permanent remote-work policy.

Three traps. Spellbook’s own caveat is that general models “may hallucinate laws” and lack “state-specific labor code awareness”. Jurisdiction gets skipped because, in Clio’s words, “You’ve been thinking about Texas employment law all morning. The AI hasn’t.” And clients arrive with their own AI drafts: one ChatGPT handbook had no anti-harassment section, discovered mid-investigation.

The story. In Mid Central Operating Engineers Health and Welfare Fund v. HoosierVac (S.D. Ind., 28 May 2025) three briefs with non-existent citations cost the lawyer a $6,000 sanction, reduced from a recommended $15,000. Employment is the fourth-largest subject area in Charlotin’s database.

Act as a [state] employment lawyer advising the employer. Audit the handbook section below against current [state] and federal law as at [date]. Output a table with exactly these columns: Topic | Coverage (Present / Partial / Missing) | Section | Recommendation. Then list every provision that is outdated or unlawful, quoting the words. Cite the controlling statute or regulation for each recommendation and tag it [VERIFY]; if you are not certain a rule exists, write "UNVERIFIED" rather than guessing.

Handbook section:
[paste]

Full guide: AI for employment lawyers.

IP: USPTO rules, trademark clearance and the export trap

Three tasks that work. An office-action response outline with a claim chart of the examiner’s mapping against the references you supply; a clearance-search summary that groups hits you found in the register by conflict risk; and brand-name brainstorming, provided a human makes and documents the adoption decision, since the trademark owner is whoever controls the goods, “not the software that drew the logo”. The USPTO’s examiners use AI too: Class ACT has analysed 250,000 trademark applications since its launch on 19 March 2026, and Scout LLM reached full adoption on 1 July 2026, under the motto “Examining attorneys lead, tools support.”

Three traps. The USPTO’s April 2024 guidance (89 Fed. Reg. 25609) is blunt: “Simply relying on the accuracy of an AI tool is not a reasonable inquiry” under 37 CFR 11.18(b). The same notice warns that AI tools on non-US servers may breach foreign-filing-licence rules under 37 CFR 5.11©. And generative naming clusters on descriptive words, exactly the ones the USPTO refuses.

The story. In Concord Music v. Anthropic (N.D. Cal., 23 May 2025) a Latham associate asked Claude “to provide a properly formatted legal citation” for an expert declaration. The link was right, the author and title were invented, and the “manual citation check did not catch that error”. The portion was struck. Formatting requests still generate content.

Using the attached Office Action, the claims as filed and the full text of the cited references, produce for each rejected claim: the rejection type and the examiner's reasoning quoted; a claim chart mapping each element to the reference passage actually cited; the elements you cannot find at those passages (quote what is there instead); and candidate arguments, each tied to a specific passage. Do not draft the response and do not cite any reference I have not supplied.

Full guide: AI for IP, patent and trademark lawyers.

Real estate: lease abstraction with a clause-reference table

Three tasks that work. Bryckel, which sells abstraction software, says consumer models work for “summarizing individual clauses, extracting rent schedules, identifying key dates, translating complex legal language into plain English, drafting preliminary abstraction tables”. The discipline that makes this safe is a table with a clause-reference column for every cell, so review becomes spot-checking.

Three traps. Length: retail leases run 40 to 80 pages, office 60 to 120, ground leases 100 or more, and a naive paste silently truncates. Invention: models “may invent renewal terms”, and “the same prompt applied to the same document may produce slightly different outputs across multiple runs”. Confidentiality: leases contain tenant sales reporting and personal data, so a consumer tier is the wrong place for them.

The story. Real estate has no Mata yet. Its failure is quieter: an invented renewal option in an abstract that nobody checks until the option date has passed.

Abstract the attached lease into a table with exactly these columns: Field | Extracted value | Clause reference (section and page) | Confidence (High/Medium/Low). Fields: tenant; landlord; premises; commencement; expiry; renewal options; base rent and escalation; security deposit; permitted use; assignment and subletting; break rights; repair obligations. Write NOT FOUND rather than inferring. After the table, list every amendment, side letter or estoppel the lease refers to.

Full guide: AI for real estate lawyers.

Tax: the “technically perfect sham”

Three tasks that work. Bloomberg Tax draws the line at “parsing statutes and drafting preliminary memoranda” under supervision. Thomson Reuters’ Senay Redda gives “If you spun off a C corp, how does it become an S corp?” as a question that yields “a step-by-step answer”; high-volume indirect-tax questions (“Can I get the rate? Is it exempt?”) suit a chatbot too.

Three traps. Bloomberg Tax’s Pramod Kumar Siva shows how a model can build an IRC § 351 exchange that meets every literal requirement and is still a “technically perfect sham” under the economic-substance doctrine. Arithmetic: models “still tend to trip over things that require that kind of calculation” (Will Matthews). And “jurisdictional flattening”, federal law over-represented and state and international law under-represented in training data.

The story. As reported by Bloomberg Tax, the US Tax Court struck a pretrial memorandum in Thomas v. Commissioner for relying on fabricated cases, and a separate matter built on a non-existent safe harbour ended in an IRC § 6662 accuracy-related penalty. “Hallucinated authority isn’t merely a drafting defect, but a substantive legal failure.”

Draft a tax memorandum on [question] under [jurisdiction, tax year]. Sections: Issue; Short answer with a confidence statement; Facts (from the facts below only); Law, with every code section, regulation, ruling and case tagged [VERIFY] and the exact section number; Analysis distinguishing the taxpayer's position from the authority's likely position; Risks and disclosure. Finish with an appendix listing every authority, the proposition it supports, the quotation relied on, and an empty column "Verified by / date".

Facts:
[paste, anonymised]

Full guide: AI for tax lawyers.

Family and estate planning: the client-side privilege trap

Three tasks that work. Financial-disclosure gap analysis and payment tracing from bank statements into a spreadsheet; issue lists and intake questionnaires for separation agreements; and, on the estate side, turning consented meeting notes into an instruction sheet that lists the contingencies the client has not addressed. The house rule from one family practice sums it up: “Use AI for preparation, not final product.”

Three traps. Your client is using ChatGPT too. Ward and Smith lists what divorce clients actually do with it: summarise attorney communications, brainstorm custody arguments, analyse the spouse’s discovery responses, and warns them to expect subpoenas for “all communications with AI-based tools, including prompts, inputs, and outputs”. AI wills fail formalities: California’s Probate Code requires two witnesses present at the same time (§ 6110) and presumes against a drafter-beneficiary (§ 21380), so “an adult child prompting ChatGPT to draft a trust amendment favoring themselves” is walking into a will contest.

The story. Ridley Law’s description of an AI will contest is the one to show clients: the chatbot transcript becomes evidence, and the other side “can read your own words, typed to a chatbot, laying out exactly what you were thinking and why.” Family law also featured in the Westlaw sweep above, including In re Marriage of Haibt (Colo. Ct. App.).

Compare the other party's financial statement with the documents produced (both anonymised, attached). Output a table: Statement item | Amount declared | Supporting document and page | Match / Discrepancy / No support. Then list accounts referenced in documents but not declared, income visible in bank statements but absent from the statement, and transfers to third parties over [amount]. Use neutral language and make no allegation of dishonesty. State how many pages you could not read.

Full guides: AI for family lawyers and AI for estate planning lawyers.

Criminal defence: from “should we” to “duty to”

Three tasks that work. A discovery and body-cam timeline with Bates numbers and timecodes, Brady/Giglio flags and a suppression-issue matrix grouped by Fourth, Fifth and Sixth Amendment trigger; a sentencing memo whose mitigation narrative is “sourced from client records (not fabricated)”; and cross-examination outlines with an impeachment matrix. A September 2025 survey of 511 defence professionals found 71% had used AI and 26% had used it to find witness-statement inconsistencies.

Three traps. Padilla advice on immigration consequences cannot be delegated to a model. Body-cam summaries “miss context, tone, and visual detail”, so the attorney watches the key segments. And privilege: in United States v. Heppner (S.D.N.Y., February 2026) the FBI seized a defendant’s consumer-Claude exchanges and Judge Rakoff held that “Because Claude is not an attorney, that alone disposes of Heppner’s claim of privilege.”

The story. On 9 September 2026 the New Mexico Supreme Court fined Santa Fe lawyer Stephen Aarons $5,000 and held him in contempt over a murder-appeal brief that “contained false testimony from wholly fabricated witnesses”; he had fed the transcript to ChatGPT expecting “a bulletproof summary”. Meanwhile NACDL’s white paper Parity in Practice: The Defender’s Duty to Ethically Use AI argues the opposite risk: “AI is already in the courtroom, and prosecutors’ offices are using it to manage evidence.”

From the attached discovery (police reports, body-cam transcripts with timecodes, witness statements), build a chronological timeline: Timecode or Bates | Source | Event | Officers and witnesses present | Statement quoted verbatim | Potential suppression issue (4th/5th/6th Amendment trigger, one line) | Potential Brady/Giglio material (describe, do not conclude). Mark anything derived from a transcript rather than video as TRANSCRIPT ONLY - REVIEW FOOTAGE. Do not summarise; quote.

Full guide: AI for criminal defense lawyers.

Personal injury: chronologies, demand letters and HIPAA

Three tasks that work. Medical-record chronologies, where manual review of a moderately complex file takes “10 to 20 hours” on EvenUp’s estimate; demand-letter sections built one at a time (case summary, liability, damages); and treatment-gap and pre-existing-condition analysis. PI is the most AI-saturated plaintiff practice: 37% of PI lawyers use generative AI against 31% overall.

Three traps. HIPAA: consumer ChatGPT tiers do not come with a business associate agreement, so medical records need an enterprise or PI-specific tool under a BAA. Valuation, in one PI lawyer’s words on Reddit: “AI substantially over values cases if you ask it about what a reasonable settlement should be”. And missing facts: “If critical facts are missing or misunderstood, the resulting draft may contain legal inaccuracies.”

The story. In Wadsworth v. Walmart (D. Wyo., 24 February 2025) a Morgan & Morgan lawyer uploaded a motion in limine to the firm’s in-house MX2.law platform with the prompt “add to this Motion in Limine Federal Case law from Wyoming”. Eight of nine cases were fake; two partners e-signed without reading. Sanctions: $3,000 and pro hac vice revoked for the drafter, $1,000 each for the signers, and an email to 1,000-plus lawyers that fake citations can mean termination.

From the attached medical records (OCR'd, handled in our BAA-covered tool), build a chronology: Date | Provider | Encounter type | Complaint or diagnosis (quoted) | Treatment | Work restrictions | Billed amount | Page. Then list treatment gaps over 30 days with the pages before and after, pre-existing conditions mentioned, and inconsistencies between providers. Do not estimate general damages or settlement value.

Full guide: AI for personal injury lawyers.

Solo and small firms: the 8% problem

Clio’s 2025 solo and small-firm report found that only 8% of solos and 4% of small firms had adopted AI “widely or universally”; most use “low barrier tools like ChatGPT”. Clio’s 2026 numbers, via the NC Bar, show the economics: 71% of solos and 75% of small firms use AI, but only 32% and 31% report a revenue increase, 86% and 78% have not adjusted pricing, and 57% and 55% have no AI policy.

Now put that next to Stanford’s analysis of who submits AI-tainted filings: 90% of US lawyer hallucination cases come from solo or 25-lawyer-or-smaller firms, and solos alone account for 50.4% of the firms involved. The mechanism is the same at any size: Amir Mostafavi “enhanced” his appellate briefs with ChatGPT, ran them through other AIs to check, did not read them, and was fined $10,000 in Noland v. Land of the Free for 21 fabricated quotations out of 23.

The safe solo workflow is short: a no-training tier rather than a consumer account, anonymisation before anything client-identifying goes in, small drafting tasks from clean facts, and no citation leaves the office until you have opened the case yourself. The Reddit divorce lawyer quoted at the top has the first half right: standard petitions and motions, no research. The second half is reading every page before it goes out.

How to read the practice pages

Every guide in this cluster follows this skeleton: tasks with a source, traps, the sanction story, prompts and a verification step, jurisdiction first. What no card can do is practise the workflow with you. That is why AI Lab for Lawyers lets each participant choose their own practice-area exercises: a tax lawyer builds a memo protocol while a litigator builds a chronology, and both leave with something usable on Monday.

Where to go next: litigators of any specialism should add the litigation guide; anyone doing removal or asylum work should read the immigration guide; and the cross-practice workflow guide shows the task-level version of everything above. If you would rather learn by doing, the four live sessions of AI Lab for Lawyers are built for that.