GPT for Lawyers: Research, Drafts, and Verification

GPT and similar large-language-model tools can draft a memo, summarize case law, and suggest clause alternatives faster than any assistant. That speed is useful, and it is also where the main risk lives: a confident answer that cites a case that does not exist. Every GPT output in a legal workflow needs attorney review against primary sources before it leaves the office. Tools like fenbs keep approved working instructions and matter-independent review rules outside any chat session, so the next research run starts from the same verified baseline.

This page covers practical GPT workflows for legal professionals, where source verification fits in, and how to handle confidentiality. Related pages cover AI for lawyers training and AI for immigration lawyers.

What GPT can realistically do for legal work

Think of GPT as a fast first drafter and a research assistant that never gets tired, not as an authority. It is well suited to producing a first outline of a memorandum, suggesting discovery questions from a fact pattern, translating dense regulatory language into plain English for a client, and flagging contract scenarios you describe.

• Drafting first versions of letters, memos, and routine agreements for attorney review.
• Summarizing a long document or deposition transcript into key points.
• Generating a list of research questions from a fact pattern.
• Explaining regulatory provisions in plain language for client communication.
• Comparing two contract drafts and listing substantive differences.

What GPT does poorly without guidance: citing specific cases accurately, tracking recent statutory changes, knowing your jurisdiction's local rules, and applying your firm's preferred style. Those gaps are exactly where a verification step and firm-specific instructions matter most.

Why source verification is non-negotiable

Large language models produce text that reads like authoritative legal writing even when the cited case does not exist. Courts have sanctioned attorneys for filing briefs that cited fabricated cases. The ABA's Model Rules of Professional Conduct require competence, and the duty to supervise applies to AI-generated drafts exactly as it applies to work produced by junior associates.

Build the verification step into the workflow, not as a final check after the draft is done, but for each citation as it appears. Open the case or statute in a verified legal database before relying on it. If a tool cannot give you a primary source you can open, treat its output as a research lead, not a confirmed authority.

Record what you verified and what remains open. A note that says "confirmed current in Westlaw, October 2026" is more useful than a polished draft that carries unchecked citations. If a position cannot be verified, say so explicitly in the document.

Confidentiality and tool selection

Before entering any client information into a GPT interface, check the tool's data use terms. Consumer-facing AI products may use inputs to train future models. Enterprise and API plans often provide data isolation, but the terms differ by vendor and by plan. Check the current documentation of any product you use.

One approach that sidesteps the problem: use AI on the structure and legal analysis without entering identifying client facts. Describe a scenario with generic placeholders ("a manufacturing company in Ohio") and substitute actual client details only when you prepare the final document.

Your firm's ethics counsel or a state bar ethics hotline can advise on specific tools. The ABA has issued formal opinions on AI use in legal practice; check their current guidance rather than relying on summaries that may be months out of date.

A verification-first research workflow

The following sequence keeps the AI in the role of drafter and summarizer while the attorney controls the authoritative record.

1. Write the research question precisely: jurisdiction, governing statute or common-law doctrine, and the specific fact issue. A vague prompt produces a generic answer.
2. Ask GPT to generate the main authorities and arguments on each side, and to flag areas where it is uncertain or where the law may have changed recently.
3. Open each cited case or statute in a primary legal database and verify it exists, says what GPT described, and is still good law.
4. Note which authorities survive verification and which do not. Set aside unverified citations as research leads to check independently.
5. Draft the memo or argument from verified authorities, using GPT to help with phrasing or transitions once the substance is confirmed.

This workflow applies equally to contract drafting: generate alternatives, check each clause against what courts in the relevant jurisdiction have enforced, then record the choices that survive review. The page on AI for lawyers drafting goes deeper into that specific sequence.

Approved instructions that carry between sessions

Most attorneys repeat the same setup at the start of each AI session: verify citations, use Delaware law, keep client names out of the interface, format headings this way. Retyping that setup wastes time and leaves room for variation. A better approach stores the approved instructions outside any one chat and loads them at the start of every session.

Separating matter-independent instructions from matter-specific context also matters. "Always verify citations in a primary database" is an instruction that applies to every matter. "This transaction is governed by New York law and the other party has objected to standard arbitration clauses" applies only to that matter. Keeping them apart prevents a matter-specific instruction from accidentally affecting unrelated work.

Firm-wide review rules that any session applies

When a GPT draft survives review, the review decisions are worth recording. Which clause language was accepted, which was revised, and which was rejected? Over time that record captures what the firm's attorneys have approved in practice, and it shortens the review cycle for similar drafts.

Some of those decisions rise to the level of standing rules: always verify citations, do not enter client names into consumer AI tools, a partner must approve AI-drafted clauses in M&A agreements. These are the instructions that should load automatically at the start of every AI session, so they apply consistently regardless of who is running the session.

How to do this with fenbs

1. Create a free account and set up a project for your practice area or a recurring matter type.
2. Add your matter-independent rules as Decisions and rules: citation verification requirement, confidentiality handling, preferred heading format. Mark each as a standing rule.
3. For an active matter, add tasks for each research or drafting job. Note verified sources as you confirm them and record review decisions as you make them.
4. Connect your AI assistant from Integrations. At the start of each session it reads the rules and Where we left off.
5. Before closing a session, save progress on each task: what was verified, what remains open, and the next step. The next session picks up from there.

For a broader view of AI assistants in legal practice, see AI agents for lawyers and what an AI brain is.