Title - AI Training for Law Firms: Drafting, Review and Source Checking
URL - https://ainora.lt/ai-training-for-companies/for-law-firms
Last Updated: 2026-09-06

# AI training for law firms

A practical session for a legal team: assistance with drafting and review, summaries of long files, the limits of research assistance, and confidentiality. One failure mode gets a section of its own, because in this profession it is the costliest: the source that does not exist.

**Talk through a team programme:** https://ainora.lt/contact?from=ai-training-for-law-firms

The full training programme is at AI training for companies (https://ainora.lt/ai-training-for-companies). The Lithuanian edition of this page is DI mokymai teisininkams (https://ainora.lt/lt/mokymai/teisininkams).

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## What is AI training for law firms?

**AI training for law firms** is a practical programme for a legal team that teaches the use of AI tools in drafting, review and summarising, and at the same time establishes the routine by which every output is checked before it is used. It differs from a general AI course in two ways: the work is done on your own templates, and most of the time goes on source checking and confidentiality.

The reason is specific to the profession. In legal work an output rests on a source, and the form of a legal citation is exceptionally regular. A language model reproduces form very well, so it can produce a reference that looks immaculate from every angle and corresponds to no real document. This is the one area where we recommend a team have a written rule before its first working day with the tool. The systems side for practices is on AI for law firms (https://ainora.lt/industries/law-firms).

## The same tool works in both directions

The strongest argument for training is not a productivity figure. It is that the same instrument, in the same team, helps or harms depending on the type of task and on whether the person can see the difference.

- **+12.2%** more tasks completed with AI where the task sat inside the model's capability frontier (pre-registered experiment, 758 consultants). (Dell'Acqua et al., HBS WP 24-013)
- **19 pp** less likely to produce a correct solution, in the same experiment, on a task selected to be outside that frontier. (Dell'Acqua et al., HBS WP 24-013)
- **20%** of studied organisations experienced breaches linked to shadow AI, meaning unsanctioned tools adopted without IT or security oversight. (IBM, 2025)
- **97%** of breached organisations that had an AI-related security incident say they lacked proper AI access controls. (IBM, 2025)

### What the evidence behind this page actually is

| Source | Year | Sample | Key finding | Confidence |
|---|---|---|---|---|
| Dell'Acqua et al., HBS WP 24-013 | 2023 | 758 consultants, pre-registered field experiment, three randomised conditions | Inside the frontier: 12.2% more tasks, 25.1% faster. Outside it: 19 percentage points less likely to be correct | High for the causal direction, because of randomisation. It is a working paper, and the tasks are consulting tasks rather than legal ones |
| IBM, Cost of a Data Breach Report 2025 | 2025 | 600 breached organisations in 17 industries worldwide, researched by the Ponemon Institute | 20% of studied organisations had breaches linked to shadow AI, adding as much as USD 670K to the average breach cost | Medium. Vendor-published, based on interviews with breached organisations, so it describes those organisations rather than a general population |
| Mata v. Avianca, Inc., S.D.N.Y. | 2023 | One case, 22-cv-1461 (PKC), opinion and order on sanctions of 22 June 2023 | A penalty of USD 5,000 imposed jointly and severally on two attorneys and their firm over non-existent opinions with fake quotes and citations | High as to what the court decided, because it is the primary record. It is a single United States case and establishes no EU or UK precedent |

None of these sources measured AI use in a European law firm. We publish them as evidence about a mechanism, not as a claim about your practice.

Sources: Dell'Acqua et al., HBS Working Paper 24-013 (https://mitsloan.mit.edu/sites/default/files/2023-10/SSRN-id4573321.pdf), IBM, Cost of a Data Breach Report 2025 (https://www.ibm.com/think/insights/data-matters/cost-of-a-data-breach), IBM X-Force (https://www.ibm.com/think/x-force/2025-cost-of-a-data-breach-navigating-ai), Mata v. Avianca, Inc. (https://storage.courtlistener.com/recap/gov.uscourts.nysd.575368/gov.uscourts.nysd.575368.54.0.pdf).

## Where AI helps in legal work

The session works through four groups of task. They are chosen on the same criterion as in our other programmes: it must be possible to check the result more cheaply than doing the work again.

**1. Assistance with drafting.** A first draft against the firm's template, alternative formulations of a clause, a version of a letter to a client, the structure of an internal memo. The model is strong here because the task is linguistic. In the session we work on your templates, so a draft matches the firm's style straight away rather than a generic internet standard that then has to be rewritten.

**2. Review and comparing two versions.** Finding missing clauses against a checklist, spotting inconsistent terms and definitions, collecting the differences between two versions into one table. The boundary we repeat throughout the session: the model says where to look, and the lawyer decides whether a clause is acceptable. Review assistance is attention direction, not an opinion.

**3. Summaries of long documents.** A summary of case material, correspondence or an expert report with references to specific pages. The only method we teach: ask for exact quotations from the uploaded document in a separate block first, and only then for the summary. A fabricated quotation is found by searching the document in seconds; a fabricated conclusion would have to be checked in full.

**4. Research assistance.** Explaining a concept in plain words, listing the arguments on both sides, drawing up questions before a client meeting, forming hypotheses about where to look. This is help with thinking, not a source. The gap between this group and the previous three is the widest on the page.

What is not in this programme: legal advice, a view on any particular matter, and promises that AI will replace a lawyer's judgement. We are an AI implementation company, so we teach people to work with the tool and leave legal decisions to the people accountable for them.

## Fabricated citations: the error that costs most in this profession

A language model holds no register of sources. It reproduces the form of text, and the form of a legal citation is among the most regular anybody could invent: party names, case number, year, court, page. So it can produce a reference that looks tidy from every side and simply does not exist. The same is true of an article number, a judgment date and a direct quotation from a text.

The second half of the error is the half that does the damage: when doubt arises, the person asks the same model whether the reference is real. The model confirms it and adds detail, because it is reproducing form again. So the training states the rule strictly and without exception: a reference counts as checked only when a person has opened it in the primary source and read it. Confirmation from inside the same chat window is not a check.

> **The documented case the training is built on.** On 22 June 2023 the United States District Court for the Southern District of New York, in Mata v. Avianca, Inc., imposed a penalty of USD 5,000 jointly and severally on two attorneys and their law firm. The opinion records that they submitted non-existent judicial opinions with fake quotes and citations, and then continued to stand by the fake opinions after judicial orders called their existence into question. The court was careful about the tool itself: "Technological advances are commonplace and there is nothing inherently improper about using a reliable artificial intelligence tool for assistance. But existing rules impose a gatekeeping role on attorneys to ensure the accuracy of their filings." This is a United States case, not an EU or UK precedent, and we do not present it as one. Its value is that the mechanism of the error, and the professional duty it collides with, are set down in a public primary record.
> Source: Mata v. Avianca, Inc., S.D.N.Y., 22-cv-1461 (PKC), 22 June 2023 (https://storage.courtlistener.com/recap/gov.uscourts.nysd.575368/gov.uscourts.nysd.575368.54.0.pdf)

One exercise follows from that, and we never skip it. Participants are handed a summary containing several references, some real and some correctly formed but non-existent, and asked to separate them inside a set time and then write down what allowed them to tell.

## Research assistance and where it ends

This table is filled in during the session with your team's own tasks. The aim is that the line between "help with thinking" and "a source" becomes automatic.

| Task | Where AI fits | What must be checked |
|---|---|---|
| Explaining a concept in plain words | Fits as a starting point, especially in a new area or when something has to be put simply for a client | Every definition is compared against the text in force before it is used in a document |
| Setting out both sides of an argument | Fits for preparation: the model produces the opposing logic and the weak points quickly | Whether each argument rests on a real rule; an invented rule inside an argument looks exactly as tidy as a real one |
| A reference to a judgment | Does not fit as a source; fits only as a hint about what to look for in the primary database | The reference is opened in the source and read; confirmation from the same model does not count as a check |
| A direct quotation from a document | Fits only where the document has been uploaded and the quotation is asked for from its text | The quotation is found in the document character by character; a quotation not found is removed |
| A question about the version currently in force | Does not fit without an uploaded text: the model answers from training data and does not change its tone | The version is checked in the official source, and the model's answer is used only to summarise the uploaded text |
| An overview of foreign law | Fits for initial orientation, when the point is to understand which questions arise at all | Every statement is checked in a local source or with a practitioner in that jurisdiction |
| Calculating a time limit | Does not fit: calculating in a chat window is one of the most unnecessary causes of error there is | The limit is calculated by hand or in the system and reconciled against the procedural document |

## What never goes into a consumer tool

A legal team's confidentiality perimeter is wider than data protection: it covers client identity, case material, negotiating positions and internal assessments, even where none of it contains personal data. So the decision about what may go into an external tool is the firm's decision, and in the training we help you write it down rather than take it for you.

During the session we write down four things: what goes up only anonymised, what is handled only in a company environment with agreed data retention terms, what nobody uploads, and who is told if too much went through by accident. In practice the last point is the most important, because without it a member of staff simply says nothing about a mistake. We recommend reviewing the boundary every six months. The technical side is on the security page (https://ainora.lt/security), the vendor-by-vendor picture in does your AI vendor train on your data (https://ainora.lt/blog/does-your-ai-vendor-train-on-your-data), and the full document structure on the internal AI use policy (https://ainora.lt/ai-training-for-companies/ai-usage-policy).

> **Why "do not upload sensitive documents" is not enough on its own.** In IBM's Cost of a Data Breach Report 2025, which analysed 600 breached organisations in 17 industries around the world, one in five studied organisations experienced breaches linked to shadow AI, meaning unsanctioned AI tools adopted by employees without IT or security oversight, and those incidents added as much as USD 670,000 to the average breach cost while disproportionately exposing customer personal data and intellectual property. Among breached organisations that experienced an AI-related security incident, 97 percent say they lacked proper AI access controls, and 63 percent of the organisations researched had no AI governance policies at all. A prohibition with no alternative works badly: where there is no convenient approved tool, the work moves into personal accounts that nobody can see.
> Source: IBM, Cost of a Data Breach Report 2025 (https://www.ibm.com/think/insights/data-matters/cost-of-a-data-breach)

## A checking routine a legal team remembers

1. **Upload the source rather than trusting memory.** A task starts from a document or an official text. A question with no uploaded source is a question put to the model's memory, and in legal work that is the wrong source.
2. **Quotations first, conclusion second.** Ask for exact quotations from the uploaded text in a separate block first, and only then for the summary or the proposal.
3. **Allow the answer "not found".** State in the task that a missing clause must be marked as not found rather than guessed. Without that sentence the model fills the gap with plausible text that reads like everything around it.
4. **Every reference is opened in the source.** Case number, article, date and page are checked in the primary database. This step is not skipped even when the reference looks familiar, and particularly then.
5. **Do not check by asking the same model.** Asked whether a reference is real, the model is inclined to confirm it and add detail. Checking means an external source, not a second question in the same conversation.
6. **Write down what went wrong.** Every error anybody notices goes onto a shared team list. After a few weeks that list becomes the firm's internal guidance.

> **Who is responsible for the output.** DigComp 3.0, the fifth edition of the European Digital Competence Framework published by the European Commission's Joint Research Centre in 2025, states it directly in competence statement CS1.2.10: "Recognise that AI systems may produce output which is inaccurate, even if it may seem plausible, and that the human using the AI system is responsible for checking the quality and validity of information and content generated."
> Source: European Commission JRC, DigComp 3.0 (https://publications.jrc.ec.europa.eu/repository/bitstream/JRC144121/JRC144121_01.pdf)

## The session programme for a legal team

A basic session runs three to four hours; an extended one splits into two parts with a week in between.

- **Why the model invents a source.** Enough theory for a lawyer to understand the mechanism and stop treating it as a random glitch. No neural network architecture.
- **Work on your own templates.** Everyone works with anonymised firm documents and their own real tasks, rather than watching a demonstration on a screen.
- **The source-separation exercise.** Real references and correctly formed but non-existent ones inside one document. This exercise replaces an hour of theory about reliability.
- **The confidentiality boundary in writing.** During the session we write down what goes up, what does not, and who is told about a mistake.
- **Scenarios for the next working day.** Each participant leaves with three to five scenarios they will use immediately, and one they have deliberately decided not to use.
- **A return session.** After four to six weeks we come back to the same processes and look at which scenarios stuck.

The decision layer for managing partners is covered separately on AI training for executives (https://ainora.lt/ai-training-for-companies/for-executives). The sibling programme for finance teams is AI training for accountants (https://ainora.lt/ai-training-for-companies/for-accountants). Terms are explained in the AI glossary (https://ainora.lt/blog/ai-glossary-voice-ai-terms), and what Article 4 of the EU AI Act actually says is in our explainer (https://ainora.lt/blog/eu-ai-act-article-4-ai-literacy). Scope and price are agreed per engagement on a call.

## Frequently asked questions

**What is AI training for law firms?**
A hands-on session for a legal team, worked on that team's own documents: drafts of contracts and procedural documents, review, summaries of long files, and internal memos. Most of the programme is about source checking and confidentiality.

**Can AI prepare a procedural document?**
It can prepare a draft, not a document you can file. A language model produces the most probable continuation, not a verified legal source, so every reference, every quotation and every statement about a rule in force has to be checked in the primary source. Accountability for the content of a filed document stays with the lawyer.

**Why does AI invent judgments and article numbers?**
The model learns the shape of text, and the shape of a legal citation is exceptionally regular. So it can produce a reference that matches the form perfectly and matches no real document, and deliver it in the same tone as a real one.

**Have courts actually sanctioned lawyers over AI-invented citations?**
Yes, and at least one instance is documented in a public court record. On 22 June 2023 the United States District Court for the Southern District of New York, in Mata v. Avianca, Inc., imposed a penalty of USD 5,000 jointly and severally on two attorneys and their law firm. That is a United States case, not an EU or UK precedent, and we do not present it as one.

**What can be uploaded into an AI tool when working on a client matter?**
The firm sets that boundary itself, and in the training we help you write it down. The starting position we suggest: client identity, case material, negotiating positions and internal assessments do not go into a consumer account.

**Can AI be used for contract review?**
For review assistance yes, for the decision no. The model is good at finding places worth looking at. The assessment of whether a clause is acceptable is made by the lawyer.

**How long does training a legal team take?**
A basic session runs three to four hours; an extended programme splits into two parts with a week in between. In BCG's 2025 survey of 10,635 respondents, the share of employees who are regular AI users rose from 18 percent with no training to 89 percent among those with more than ten hours.

**Does the EU AI Act require lawyers to be trained?**
Not in the form the market usually claims, though an obligation does exist. Article 4 binds and has applied since 2 February 2025. It is not listed in Article 99(4), so it carries no EU-level fine ceiling of EUR 15 million or 3 percent of turnover, and any consequence runs instead through national law under Article 99(1), a paragraph the 2026 amendment widened rather than narrowed. This is general information, not legal advice.

## Start with your own documents

An hour on a call to look at which of your legal team's tasks suit AI, which do not, and what the checking routine will look like. No obligation.

- Talk through a team programme: https://ainora.lt/contact?from=ai-training-for-law-firms
- All AI training for companies: https://ainora.lt/ai-training-for-companies
- AI for law firms: https://ainora.lt/industries/law-firms
- Lithuanian edition: https://ainora.lt/lt/mokymai/teisininkams

## About AINORA

AINORA, MB (ainora.lt) builds and operates AI voice agents, internal knowledge systems and process automation for European businesses. Training is delivered as part of a deployment rather than as a course catalogue.

- Website: https://ainora.lt
- Email: info@ainora.lt
- Contact: https://ainora.lt/contact

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Note: scope and programme are agreed in an individual conversation. Information about the EU AI Act and about court decisions is general in nature and is not legal advice.