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Do not put a legal AI assistant on live matters because a demonstration looked convincing. First test the exact task, with representative material that is fictional, public, redacted, or expressly approved; compare every answer with a lawyer-checked reference; verify authorities independently; and review the product’s data, security, and contract terms. Then define human-review and escalation rules before a limited pilot. The lawyer remains responsible for the advice and work product.
Performance is task-, tool-, and jurisdiction-specific. A system that summarizes a routine agreement may be unsuitable for jurisdiction-specific research, a filing deadline, or advice based on conflicting facts. A passing result on one prompt is evidence about that prompt—not proof of general reliability.
What a proper evaluation is—and why a polished demo is not enough
An evaluation asks whether a particular tool can perform a defined job, for your jurisdiction and practice, at an error rate and review burden your firm can safely manage. It also asks whether the way the service handles prompts and documents is compatible with confidentiality, privilege, privacy obligations, client instructions, and your engagement terms.
The American Bar Association’s Formal Opinion 512 (July 29, 2024) says a lawyer must have a reasonable understanding of a generative-AI tool’s benefits and risks, with the amount of verification depending on the task and the tool. Its warning is practical: “This is not a static undertaking.” Product versions, settings, source collections, and terms change, so an approval decision needs an owner and a review date.
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Legal-specific branding does not remove the need to test. In a preregistered 2024 study of more than 200 legal queries and three named products, the tested systems produced hallucinated or unsupported material between 17% and 33% of the time. Those figures describe that study’s versions, prompts, and method; they are not a market-wide probability or a prediction for your workflow.
1. Define the proposed use and its risk boundary
Write a one-page use case before opening an account. A vague goal such as “use AI for legal work” cannot produce a meaningful pass or fail decision. State the task, users, jurisdiction, source material, expected benefit, and consequence if the answer is wrong or incomplete.
Classify the task
| Task category | Typical examples | Questions that determine risk |
|---|---|---|
| Low-consequence assistance | Brainstorming headings, generating interview questions, or rephrasing a non-confidential paragraph | Could an unreviewed error affect a client, a deadline, a representation, or a legal conclusion? |
| Information extraction | Finding indemnity, termination, governing-law, or notice clauses in a contract set | How will you detect a missed clause, a false positive, or a clause whose meaning depends on definitions elsewhere? |
| Summarization and chronology | Reducing a deposition, discovery production, or public decision to a digest | What facts may not be omitted, and how will a reviewer check the summary against the source? |
| Drafting | Preparing a first draft of a memo, letter, pleading section, or contract language | Who checks every factual assertion, citation, defined term, procedural requirement, and jurisdictional assumption? |
| Legal research and advice support | Finding authorities, explaining a rule, or applying law to client facts | What authoritative sources must be consulted, and what is the consequence of an invented, outdated, or out-of-jurisdiction authority? |
| High-consequence decisions | Filing-ready text, deadline calculations, advice that drives a transaction, or a position in court | Is AI limited to a reversible draft, with a qualified lawyer independently checking the result before anyone relies on it? |
Record the jurisdiction precisely: country, state or province, court, governing law, and any choice-of-law issue. Identify the practice group and user population. A tool may perform differently for employment law in one state than for commercial litigation in another, and a result that is useful for internal issue spotting may be unacceptable in a filed document.
Set an explicit boundary
- List permitted inputs, prohibited inputs, and whether any client or privileged material may be used during the pilot.
- State whether the tool may produce only suggestions or may populate a work product template.
- Define tasks that always require a lawyer to perform the work without AI assistance, such as final citation validation, deadline approval, or signing a filing.
- Specify who owns the decision, who performs the review, and who can stop the pilot.
2. Build a representative and safe test set
Early testing should use fictional files, public records, synthetic clauses, carefully redacted documents, or material for which the client has expressly approved the use. Do not upload live confidences merely to see whether the tool works. Professional guidance treats confidentiality, provider access, transfer, security, reuse, and contractual terms as separate issues; a free trial’s default settings are not a substitute for that review.
Include ordinary and adversarial examples
- Routine items: the common contract, memo, clause, or question the team expects to handle.
- Boundary cases: missing facts, ambiguous instructions, conflicting provisions, scanned pages, tables, and documents with defined terms spread across sections.
- Legal traps: an old authority, a similar case from the wrong jurisdiction, a changed statute, a false premise, or a question whose answer is “the record is insufficient.”
- Source requests: prompts that require pinpoint citations, quotations, links, or a list of authorities supporting each proposition.
- Repeat runs: identical prompts run more than once so you can see whether the answer changes without a meaningful change in input.
Have a qualified lawyer prepare or validate the expected answer before the model sees the item. For a contract-extraction test, mark every clause that should be found and every clause that should not be classified as a match. For a research test, identify the controlling authorities, contrary authorities, date limits, and jurisdictional distinctions. Preserve the source and reference answer under version control.
Keep the test reproducible
For each run, save the prompt, attached files, product name and version or model identifier shown by the service, configuration, date and time, user, output, and reviewer notes. If the interface does not disclose a version, record that fact. A later model or retrieval-index change can invalidate an earlier result; reproducible records let the firm retest rather than rely on memory.
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3. Score the output against a lawyer-reviewed reference
Do not judge quality by fluency, speed, or how confidently the answer is written. Use a scorecard in which a reviewer compares each output with the reference answer and the underlying sources.
| Dimension | What to check | Evidence to record |
|---|---|---|
| Factual accuracy | Names, dates, amounts, procedural posture, quotations, and extracted terms match the source. | Each error, its severity, and whether it could change advice. |
| Legal accuracy | The stated rule and application fit the governing jurisdiction and the facts supplied. | Correct, incorrect, uncertain, or not answerable, with the reviewing authority. |
| Completeness | Required issues, clauses, exceptions, adverse authorities, and limitations are present. | Omissions and whether a reasonable reviewer could spot them. |
| Source support | Every citation exists, is current enough for the task, and actually supports the proposition attributed to it. | Verified source, pinpoint location, and any mismatch. |
| Jurisdiction fit | The answer does not silently substitute another state, province, country, court, or version of a statute. | Jurisdictional errors and the prompt or setting that caused them. |
| Consistency | Repeat runs produce materially similar answers when inputs are unchanged. | Differences, frequency, and whether a reviewer must reconcile them. |
| Detectability | A lawyer can identify an error efficiently rather than having to recreate the work from scratch. | Review time, missed errors, and the checks needed to catch them. |
| Operational fit | The output can be exported, logged, redlined, and retained in the firm’s approved workflow. | Manual workarounds, access issues, and audit gaps. |
Use severity bands rather than a single blended percentage. A typo in a brainstorming list is not equivalent to an invented case cited in a filing. Set stop conditions in advance—for example, any fabricated authority, any missed mandatory clause, or any error that a normal reviewer would not reasonably detect ends the test for that use case until the control is redesigned.
These measures are a practical firm scorecard, not a published common benchmark. The Stanford-authored study illustrates why measurement matters: in its specific setup, Lexis+ AI answered 65% of tested queries accurately, Westlaw AI-Assisted Research answered 42%, and Ask Practical Law AI gave incomplete answers on more than 60% of queries. The study’s results should not be read as a current ranking, a guarantee for another version, or a prediction for your prompts. Read the methodology and limitations in “Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools”.
4. Verify every legal citation and source claim
A citation-shaped string is not evidence. For research and drafting tasks, make source verification a separate workflow rather than an optional spot check.
- Parse the claim. Break the sentence into the exact proposition the authority is supposed to support. A citation may support one element but not the conclusion drawn from it.
- Locate the authority independently. Open the official court, legislature, regulator, or authoritative legal database record rather than relying on a link or quotation supplied by the tool.
- Check identity and status. Confirm the case name, court, docket or reporter reference, date, statutory section, amendments, subsequent history, and whether the authority is binding for the matter.
- Read the cited passage in context. Check definitions, exceptions, procedural posture, quoted language, and whether the text concerns a different issue or jurisdiction.
- Record the result. Mark the citation supported, partially supported, unsupported, or not found, with a pinpoint and reviewer initials.
- Require correction before reuse. Remove unsupported citations and rewrite the proposition; do not leave a plausible-looking reference in a draft as a reminder to check later.
Prompts that demand “sources” can improve traceability but cannot make an unsupported answer reliable. Test whether the tool admits uncertainty, distinguishes primary from secondary authority, and says when the supplied facts are insufficient.
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5. Review privacy, security, and contract terms
Legal-specific features do not answer what happens to your data. Obtain the operative terms, privacy notice, security documentation, and product-tier commitments for the exact service you would buy. Save the versions reviewed and the date.
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| Area | Questions for the provider and your internal review |
|---|---|
| Retention | Are prompts, uploaded documents, outputs, logs, and backups retained? For how long, and can retention be disabled or shortened? |
| Training and reuse | May inputs or outputs be used to train, improve, evaluate, or otherwise develop services? Is the setting different for free, team, enterprise, API, or regional tiers? |
| Human and provider access | Can provider staff, contractors, support personnel, or reviewers see content? Under what purpose, approval, logging, and confidentiality controls? |
| Deletion | How do you delete a matter’s data, including indexes, caches, backups, and derived artifacts? What evidence or service-level commitment is provided? |
| Security controls | What encryption, identity management, tenant isolation, vulnerability management, logging, and incident-response controls apply to this product? |
| Incident notice | When and how will the firm be notified of unauthorized access, loss, or a suspected breach, and who is the contact during an incident? |
| Subprocessors | Which companies process data, where are they located, and how are changes announced or challenged? |
| Data location and transfer | In which countries or regions are data stored and processed? What cross-border transfer mechanism and privacy obligations apply? |
| Contract and audit rights | Do the terms address confidentiality, privilege, ownership, indemnity, service changes, export, audit evidence, and termination assistance? |
| Administrative controls | Can the firm enforce single sign-on, role-based access, matter separation, approved connectors, retention settings, and detailed logs? |
Match each answer to the risk of the intended use and to client instructions. If the provider gives only a marketing statement, treat the control as unverified until the binding terms or an appropriate security document says otherwise. The ABA opinion and the New Zealand Law Society’s guidance both emphasize that confidentiality and professional responsibility do not move to the provider when a lawyer uses an AI service.
6. Establish human review and escalation before the pilot
Write the review rule next to the use case, not in a general policy that users must interpret under deadline pressure. The rule should identify the reviewer’s qualifications, the checks required, and the point at which AI output must be discarded or escalated.
A practical review ladder
- Low-risk assistance: the user checks the output against the supplied material and removes confidential data not needed for the task.
- Substantive draft: a lawyer verifies every factual assertion, defined term, calculation, legal proposition, and citation, then edits the document as the responsible author.
- Research or advice: the lawyer independently confirms primary authorities, contrary authority, current law, jurisdiction, and factual assumptions before relying on the analysis.
- Filing, deadline, or client decision: a designated lawyer performs a final independent review using authoritative sources and the matter record; AI output alone cannot authorize submission or advice.
Escalate when the tool expresses high confidence despite missing facts, produces a citation that cannot be found, mixes jurisdictions, contradicts the source document, changes materially between runs, or cannot explain the basis for a conclusion. Preserve the failed output and the correction so the team can improve the test set and training.
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The Law Society of Alberta’s quick tips similarly stress understanding capabilities, monitoring output, and implementing security measures. The New Zealand Law Society states: “All lawyers are ultimately responsible for the legal services they provide.” These are jurisdiction-specific guidance documents, not a universal substitute for your regulator’s rules.
7. Compare tools without turning the decision into a brand contest
If more than one product survives the initial test, run the same blinded tasks and score them with the same rubric. Do not compare a vendor’s best demonstration with another tool’s unedited output.
| Comparison axis | Evidence to collect | Decision question |
|---|---|---|
| Accuracy and completeness | Scores on the firm’s representative tasks, including difficult and repeat cases. | Does the benefit remain after the required review time? |
| Authorities and traceability | Existence, currency, jurisdiction, pinpoint support, and transparency of source links. | Can a reviewer verify the result quickly and reliably? |
| Client-data treatment | Retention, training or reuse, provider access, deletion, subprocessors, and location for the exact tier. | Can the firm satisfy confidentiality, privilege, privacy, and client-contract requirements? |
| Security and contract | Access controls, encryption, incident terms, audit evidence, export, termination, and change notices. | Are commitments binding and operationally enforceable? |
| Jurisdiction and source fit | Coverage of the jurisdictions, practice areas, languages, document types, and source collections you actually use. | Does the tool handle your materials without silently substituting another law? |
| Reviewer visibility | Prompt and document history, citations, provenance, versioning, redlines, and audit logs. | Can a lawyer see what the system did and correct it efficiently? |
| Workflow and administration | Identity integration, matter separation, permissions, export formats, support, training, and implementation effort. | Can the firm control use at scale rather than rely on individual habits? |
| Total cost | Subscription, usage, storage, implementation, review time, training, and exit or migration costs. | Is the measurable benefit worth the full cost and residual risk? |
Where a product does not disclose a comparable value, record “not stated” and treat the uncertainty as a decision item. Never infer a security, accuracy, or jurisdictional guarantee from a product name or a feature label.
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8. Run a controlled pilot
A pilot is a limited operational experiment with a named owner, not an informal invitation for everyone to upload matters.
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- Configure the account. Enable the strongest available access, retention, logging, and data-use controls; disable connectors and sharing features that the use case does not require.
- Train participants. Demonstrate the approved workflow, prohibited inputs, citation checks, incident reporting, and how to label AI-assisted drafts.
- Use the benchmark first. Run the reference set before live work so the team knows the measured strengths and failure modes.
- Start with approved material. If client data is contemplated, obtain the required client and firm approvals and confirm that the contract and privacy analysis covers that exact data.
- Review every output. Apply the written scorecard and record time spent, corrections, omissions, and escalations.
- Hold a midpoint review. Stop or narrow the pilot if a new failure mode appears, terms change, or reviewers cannot detect errors reliably.
- Close with a decision memo. State approved uses, prohibited uses, evidence, unresolved risks, owner, training needs, and the next review date.
9. Make a documented go/no-go decision and monitor change
A go decision should be conditional: approved for named tasks, users, jurisdictions, and data classes, subject to the stated review controls. Document the measured benefit, residual risks, review burden, costs, client-communication approach, and who monitors the provider.
Re-test after a material model or retrieval change, a new product tier, a contract or privacy-policy change, a security incident, a new jurisdiction, or a workflow change. Repeat a smaller benchmark on a regular schedule and after a serious error. Keep the original and new outputs so deterioration or improvement is visible.
Competence is ongoing. ABA Formal Opinion 512 applies that principle to a fast-changing field, and the Alberta guidance recommends continued understanding and monitoring. A tool that passed six months ago may no longer meet the same standard if its model, sources, controls, or terms have changed.
Common failure modes and corrective actions
“It got the first few answers right.”
Problem: A small, easy sample hides omissions and jurisdiction errors. Correction: Add difficult, conflicting, and unanswerable cases; use a lawyer-checked reference and a pre-set stop condition.
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Problem: The citation may be invented, nonexistent, outdated, or unrelated to the proposition. Correction: Verify identity, status, jurisdiction, and the cited passage independently before reuse.
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“The enterprise label means client data is safe.”
Problem: Data practices differ by tier and may permit retention, provider access, subprocessors, or reuse. Correction: Read the operative terms and security documents for the exact service and record unanswered questions as unresolved risk.
“A human read the final paragraph.”
Problem: A superficial read may miss an omitted exception or a false premise embedded upstream. Correction: Match review depth to consequence: check the source, every citation, calculations, defined terms, and the matter record.
“The pilot uses a real matter because that is more realistic.”
Problem: Realism does not override confidentiality, privilege, privacy, or client instructions. Correction: Begin with safe representative material and obtain explicit approvals before expanding the data set.
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Jurisdiction and professional-duty cautions
Rules differ. ABA Formal Opinion 512 addresses the ABA Model Rules and is not automatically binding in every U.S. jurisdiction. Alberta guidance reflects Alberta practice, and New Zealand guidance reflects New Zealand requirements. Privacy law, court rules, procurement controls, client contracts, and privilege doctrine may impose additional conditions. Check the current regulator and court requirements where the lawyer and matter are located.
The Texas State Commission on Judicial Conduct notebook is a January 30, 2026 compilation that reproduces guidance and hallucinated-citation materials; it is not a universal rulebook. Use it, if relevant, only in the context of the applicable Texas requirements and the exact court or agency involved: Texas courts notebook PDF.
Quick Recap
A concise approval checklist
- The task, jurisdiction, users, matter sensitivity, and consequence of error are written down.
- The test set is representative, includes hard cases, and uses safe or expressly approved material.
- A qualified lawyer prepared or validated the reference answers.
- Outputs were scored for accuracy, omissions, citation support, jurisdiction, consistency, and detectability.
- Every legal authority was independently verified before reuse.
- Retention, training or reuse, access, deletion, security, subprocessors, location, incident notice, and contract terms were reviewed for the exact tier.
- Human-review, escalation, prohibited-use, and final-approval rules are operational and documented.
- The pilot has an owner, logs, a stop condition, and a reassessment trigger.
- The go/no-go decision states approved limits and unresolved risks.
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