Claude Projects for Lawyers: Build Your Firm's Project, Custom GPT or Gem in 30 Minutes (Safely)

Ethan Mollick’s one-line brief on language models: “an infinitely patient new coworker who forgets everything you tell them each new conversation, one that comes highly recommended but whose actual abilities are not that clear.” Most lawyers respond to the forgetting by re-typing, every morning: the jurisdiction, the side they act for, the house style, the instruction not to invent cases. Then they wonder why the output is generic.

Claude Projects for lawyers, ChatGPT custom GPTs and Gemini Gems exist to end the re-typing. Anthropic’s advice on its own legal plugins applies to all three: “Don’t use it out of the box… it’s at its best when you customize it with your own legal playbooks” (Mark Pike, Artificial Lawyer); the Claude for Legal README is blunter: “Skipping setup is the single most common reason a skill produces generic output.” The setup takes about 30 minutes; done wrong, it creates the neatest confidentiality leak in the building, so the order of steps matters.

What Projects, Custom GPTs and Gems actually store

All three store standing instructions (a system prompt prefixed to every chat); Projects and custom GPTs also hold knowledge files (documents the model reads in every chat). The model does not change; the context does. What differs is data handling, which follows the plan, not the feature.

Two consequences. A Project persists, so a file you would never leave in a chat history should not sit in a knowledge base either. And the tier decides everything (see is ChatGPT confidential for lawyers): in United States v. Heppner (S.D.N.Y. 2026) Judge Rakoff held a defendant’s exchanges with consumer Claude neither privileged nor work product.

Step 1: decide what is true across matters (and nothing else)

The claudeforlawyers.com workflow starts with a list and one rule for it: “keep this list to things that are true across matters” (claudeforlawyers.com). Jurisdiction defaults. Who your clients usually are. The documents you draft most. Your playbook positions. Your pet hates in drafting. How you want citations handled.

What does not belong: anything about a live client, because the Project outlives the matter and, on a shared plan, your control of who reads it. And resist loading everything: Anthropic’s own documentation warns that “more context isn’t automatically better. As token count grows, accuracy and recall degrade.”

Step 2: write instructions that lead with safety rules, not your bio

The most useful line in the claudeforlawyers.com guide: “Lead your custom instructions with the safety rules, not your bio.” The first thing a legal assistant needs to know is what it must never do. The published example opens with:

“SAFETY RULES (ALWAYS) — Flag EVERY case, statute, or rule citation as UNVERIFIED until a lawyer confirms it. Never invent facts, dates, dollar figures, or names. Leave [BRACKETS] instead. Ask before assuming when a request is ambiguous. You are a drafting aid. A licensed attorney reviews and is responsible for all output.” — claudeforlawyers.com, example Project instructions

Then voice, jurisdiction, house style and what not to do, in that order. If you would rather be interviewed than write it cold, have the model do the interviewing. Ernie Svenson: “I had Claude interview me about my background, my preferences and how I work. I told it to ask me 40 questions” (Attorney at Work).

Help me write the standing instructions for a reusable AI workspace for my practice. Interview me with up to 40 questions, in batches of ten: my practice area and jurisdictions; who my clients are; my house style and pet hates; the documents I draft most; my playbook positions; how I want citations handled; what must never appear in outputs; my verification routine; how you should behave when unsure.
Then draft the instructions with SAFETY RULES first, then VOICE, JURISDICTION defaults, HOUSE STYLE and WHAT NOT TO DO, plus a short list of anonymised knowledge files I should upload. Under 600 words.

Step 3: add an anonymised knowledge base

“A tight, anonymized clause bank beats a giant dump of old documents” (claudeforlawyers.com). Good candidates: engagement letters, demand-letter templates, intake checklists, a clause bank, your playbook. Before upload, “strip client names, matter numbers, and any privileged specifics”, and strip metadata (tracked changes, comments, properties) too. BRAK’s warning applies: removing names and addresses is often not enough “wenn sich Mandatsinformationen aus dem Kontext ergeben können”. Method in how to anonymise documents before AI.

From the attached anonymised agreements, extract every [limitation of liability / data protection / termination] clause into a clause bank: Clause ID | Deal context (our side, sector, size band) | Full clause text | Position (client-favourable / balanced / counterparty-favourable) | Defined terms it depends on.
Deduplicate near-identical clauses and note the variants. Then propose a tagging scheme so a lawyer can find the right clause in under a minute. Do not add any clause that is not in the documents.

Spellbook’s custom-GPT guide runs the same six steps, from identifying high-volume tasks to deploying and monitoring, and adds: “Sensitive information must remain secure by deploying the assistant in encrypted, access-controlled environments” (Spellbook).

Step 4: calibrate on three to five previously reviewed documents

Anthropic’s own worked example is an NDA-review Project, set up with: “I’m setting up a Project for NDA reviews at our firm. We handle about 30 NDAs a week and I want Claude to reference our standard terms, flag deviations, and maintain consistency in our review comments.” The instructions tell Claude to “reference the Standards Guide for every review, apply the three-part comment structure consistently, use correct severity indicators, flag escalation triggers explicitly, and run a quality checklist before delivering”. And before rollout: test with “3-5 previously reviewed NDAs” (Claude Academy).

Here's an NDA we received yesterday from a potential vendor. Review it against our standards guide and flag any deviations.
For each deviation: quote the clause, name the standards-guide position it departs from, rate the severity, and say whether escalation is required. Then list anything in the NDA the standards guide does not address.

Run it on documents where you already know the right answer, compare the output with what you actually did, and adjust the instructions where they diverge; Harvey gives platform pilots the same advice, test on “historic matters from 2022 to 2025 where you already know the outcome”. The Academy’s caveat: “If your playbook is thorough and your templates are up to date, Claude’s standards guide will be stronger.” The playbook is built in AI contract review against a playbook.

Step 5: maintain it every few months

“Treat the Project as version one” and revisit “every few months” (claudeforlawyers.com). Models update under you; as one r/biglaw associate put it, “a model will update and I will need to tweak the prompt again.” Keep a one-page note per Project: owner, approved tier, what must be anonymised, verification steps, and a “last tested” date with the model version. Sterling Miller’s advice to departments applies at firm scale: work “as a team to develop their own library of standard prompts tailored to the specific needs of the department” (Ten Things).

The shared-Project trap on Team and Enterprise plans

This is where the 30-minute build goes wrong. On business plans a Project is shareable: the point of a firm template, and the risk.

Three habits close it. Two tiers of Project: a shared practice-area template holding only anonymised material, and private per-matter Projects holding client documents, deleted at matter end. Retention set deliberately: on Claude Enterprise, project retention supersedes chat retention; on ChatGPT, files persist until the GPT or project is deleted. Feedback off: ratings on Claude Team and Enterprise are stored for five years unless admins disable “Rate chats”, so nobody rates a chat containing client material. More on each tool’s data handling in Claude for lawyers and ChatGPT for lawyers.

Example: an employment-firm Project (full instructions)

The claudeforlawyers.com example is for an Ohio employment practice. Below is its structure with the verbatim safety, voice, jurisdiction, style and prohibition lines, expanded into a set you can adapt; change the jurisdiction line for a London or Vienna practice.

SAFETY RULES (ALWAYS)
Flag EVERY case, statute, or rule citation as UNVERIFIED until a lawyer confirms it. Never invent facts, dates, dollar figures, or names. Leave [BRACKETS] instead. Ask before assuming when a request is ambiguous. You are a drafting aid. A licensed attorney reviews and is responsible for all output.

VOICE
Write in plain, measured English a client can read without a dictionary. Short paragraphs. No throat-clearing. Avoid "utilize," "herein," "aforementioned." Confident but never overstated; we do not guarantee outcomes.

JURISDICTION
Default authority: Ohio statutes and Sixth Circuit / Ohio courts. American spelling. No deadline assumptions unless I provide them.

HOUSE STYLE
Demand letters: facts, then liability, then damages, then demand, then response deadline. Define a party once in bold, then use the short form. Use the templates in the knowledge files.

WHAT NOT TO DO
Do not offer a legal conclusion as settled; frame options and risks. Do not draft affidavits or witness statements. Do not include any name that appears in a knowledge file in an output. If a request needs client facts you do not have, list them as [MISSING: ...] and stop.

The two closing prohibitions are additions: witness evidence must be the witness’s own words, and the “no names from knowledge files” rule stops a precedent’s parties leaking into a new client’s letter.

The playbook-in-a-Project pattern a solo fintech lawyer uses

A solo fintech lawyer on r/legaltech described the pattern that turns a Project from a memory aid into a workflow (thread): “describe in the prompt your current workflow and your wants. Be very detailed and take it through every step. Then ask it to create a playbook for you to save as a project .md file and as a skill. Upload the .md file into a project for that workflow … At the end of the workflow, ask the app to review your prior chats and update both the playbook .md file and the skill.”

His division of labour: “chat for single outputs, even if multiple steps to get there. Cowork for multiple outputs. MS Word plugin for finishing touches.” The firm runs “a Claude teams account with five seats”, opts out of training data, and for document generation gives the model “a docx template” and tells it to “follow its formatting”. The playbook improves each time the model reviews its own prior chats, and the lawyer decides what goes in. Zack Shapiro’s two-person firm runs the same logic at larger scale, described in AI-native law firms explained.

Where to go next: the frameworks behind good standing instructions are in prompt engineering for lawyers and the prompting hub; the 40-prompt library gives you tasks to run inside the Project; the interview technique is expanded in the curiosity prompt. In AI Lab for Lawyers we build a Project live on screen and review an NDA against it, so you leave with a working assistant for one recurring task rather than a slide about one.