AI in Court: A Verification Workflow for Legal Teams

Using AI in court preparation requires a verification workflow: identify the applicable instructions, check every authority and factual assertion, and have the responsible lawyer review the final filing. Treat an AI answer as material to investigate, never as proof that a case exists or supports the argument.

A tool like fenbs can keep approved process rules and verification source links available to the next assistant. The actual litigation record and confidential client material belong in systems the firm has approved for them. This guide concerns preparation and review by US legal teams; it does not establish what a particular court permits.

What is the verification sequence before filing?

1. Define the task and data boundary. Specify whether AI may suggest search terms, organize an argument, or summarize approved material, and identify the supervising lawyer.
2. Open the applicable court rules, individual judge practices, and case orders. Record the sources and any disclosure, certification, or restriction that applies.
3. Research independently. Retrieve each cited authority from a reliable legal research source or court publication, then verify quotations, support, and current treatment.
4. Compare factual assertions with the actual record. Check exhibit identifiers, dates, amounts, testimony, and the distinction between allegations and established facts.
5. Review the finished document. Resolve unsupported claims, complete required disclosures, and have the responsible lawyer approve the exact version being filed.

Create time for these steps before the filing deadline. An AI draft that takes minutes can still require substantial legal review. If the team cannot complete a required check, escalate the gap rather than describing an unverified draft as ready.

Which court instructions should the team check?

Start with the court hearing the matter and the judge assigned to it. Read the current local rules, individual practices, standing orders, and case-specific directions. Check again when the judge or procedural stage changes; a rule copied from another matter may be irrelevant.

For example, Judge Cathy Seibel’s individual practices↗ (opens in a new tab) require users of generative AI to verify propositions, quotations, and citations against the actual case. That is a specific judicial instruction, not a nationwide AI policy. Keep its scope attached to the source instead of turning it into a claim about all federal courts.

The ABA’s Formal Opinion 512↗ (opens in a new tab) discusses competence, confidentiality, supervision, and other duties when lawyers use generative AI. It interprets ABA Model Rules; lawyers must also examine the rules and guidance applicable in their jurisdiction. A technology policy cannot replace that professional assessment.

Record the source, the date checked, the matter it governs, and the person who confirmed the workflow. fenbs Decisions and rules can preserve the approved process without putting the underlying client file into an assistant’s project context.

How do you verify an AI-generated case citation?

Finding a matching case name is only the first check. Confirm the court, date, citation, procedural posture, and the text of the opinion. Then read the portion needed to determine whether the decision supports the precise proposition in the draft.

• Existence: locate the opinion itself, not another AI answer repeating the citation.
• Quotation: compare the words and pinpoint citation with the source, including omissions and surrounding qualifications.
• Authority: determine whether the opinion is controlling or persuasive for this court and issue.
• Treatment: use the firm’s approved research process to check later history, negative treatment, and relevant developments.
• Application: explain why the rule applies to the present facts and procedural posture.

Suppose an assistant finds a real appellate opinion concerning dismissal. The quoted language might describe a party’s argument rather than the court’s holding. The citation passes an existence check but fails the support check. Removing invented cases alone would not catch that error.

Keep a short verification entry for each consequential proposition: draft location, source, pinpoint, reviewer, and unresolved issue. General AI research workflows cover broader source collection; litigation adds the need to evaluate legal authority and the filing’s obligations.

Use precise check labels. “Opinion retrieved” means someone found the source; “quotation checked” means someone compared the passage; “legal support reviewed” means a lawyer assessed the proposition. Keeping those meanings separate helps another reviewer see the remaining work without assuming every source link represents completed legal analysis.

What should reviewers check beyond the case law?

Review factual statements separately from legal propositions. A source can be genuine while the draft gives the wrong date, reverses the parties, or turns a disputed statement into a finding. Follow the assertion back to the record and preserve the distinction between evidence and inference.

Check calculations and chronology directly. If an argument depends on a deadline, the lawyer should determine the governing rule and calculation rather than trusting an AI-generated date. Confirm that exhibit and transcript references still point to the intended material after revisions.

Also inspect what the model added while polishing. A smooth transition can introduce a new factual claim or silently strengthen a qualified conclusion. The final review must cover the version being submitted, including edits made after the earlier citations were checked.

How do you stop a checked draft from becoming unverified?

Identify the reviewed version and define what sends it back for review. Changing an argument, adding an authority, or rewriting a quotation should reopen the affected checks. A completed research task is not permanent approval for every later document that mentions it.

The AI approval workflow guide explains how to separate a suggestion from permission to act. For litigation, make that separation concrete: research prepared, sources verified, lawyer review pending, or approved for the specified next step. An assistant’s confidence does not move work between those states.

How to do this with fenbs

1. Create a free account and add a project for the nonconfidential process work. Use a team kanban board with suitable member roles when colleagues need access.
2. Record the supervising lawyer’s approved instructions in Decisions and rules. Name the scope and keep questions requiring legal judgment open for a person.
3. Add verification tasks and public source links. Describe the check, its owner, and its status without copying client records or sensitive matter details.
4. Connect a supported assistant through Integrations. Ask it to read the project rules first and save unfinished verification work in Where we left off.

fenbs is not a legal research service, a citation validator, or a court filing system. It is not the right fit for storing a litigation file or replacing the firm’s approved confidential recordkeeping. Its role here is keeping the agreed process and the work of following it clear.

An AI brain preserves useful work context, but preservation does not establish legal correctness. Explore the AI governance hub for related review practices, and end each filing task with a clear record of what a person actually checked.