AI legal research tools typically retrieve potentially relevant material from a legal content collection, then use a generative model to draft an answer from that material. Some provide links or citation signals so a researcher can inspect the sources. Those features make it easier to follow an answer’s trail; they do not, by themselves, prove that a case exists, applies to the question, supports the wording, or remains good law. Treat the output as a starting point and verify every authority that matters.
How does AI find relevant case law?
In broad terms, an AI research assistant turns a natural-language question into a search of legal materials and uses selected results to compose a response. This is often described as retrieval-augmented generation, or RAG: the system retrieves material from a defined collection and generates an answer using it. Exact query processing, ranking, and model architecture vary by product, and vendors do not disclose every implementation detail.
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1. It interprets the question
You might ask, “What cases say a landlord must mitigate damages after a tenant abandons a commercial lease?” The assistant has to identify the legal issue, relevant jurisdiction, and important factual or procedural limits before it can look for useful authorities. A query that omits jurisdiction or uses an imprecise legal label can send the search in the wrong direction.
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The system searches the content available to that product and account. Depending on the tool and subscription, that collection may include court decisions and statutes, administrative materials, secondary sources, editorial analysis, or practice guidance. The breadth and jurisdictional coverage of one service should not be assumed to match another. Thomson Reuters, for example, describes Westlaw Deep Research as using Westlaw and Practical Law tools and content; that is a vendor description, not independent proof that every relevant authority will be found.
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3. It synthesizes an answer and may link sources
A generative model turns retrieved material into conversational prose. Product features differ: Thomson Reuters describes CoCounsel Legal as grounded in Westlaw, Practical Law, and firm knowledge, with linked citations; Lexis describes linked citations and Shepard’s verification features. A link is useful because it lets you inspect the underlying document. It is not a guarantee that the answer accurately describes the decision, that the case is on point, or that later courts have not limited it.
Why can a linked citation still be wrong?
Errors can occur at more than one stage. A system may identify the wrong issue, retrieve an adjacent but inapplicable decision, or describe a passage inaccurately. It may also fail to account for jurisdiction, procedural posture, or the context surrounding a quoted sentence. The peer-reviewed evaluation by Magesh and co-authors describes, among other examples, a system retrieving material about “moral turpitude” in response to a question about the different concept of the “moral wrong doctrine.” The paper also discusses mistakes involving the wrong jurisdiction or legal context and generated descriptions that misstate a court passage.
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This is why “the citation opens” and “the answer is legally supported” are different checks. A source link points to a document; a legal researcher must determine whether the document supports the specific proposition and can properly be used for the issue at hand.
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Use the answer to locate candidates, then review the authorities directly before relying on them in advice, a brief, or another legal work product.
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- Open the cited document. Confirm that the case exists and that the link resolves to the decision identified in the answer.
- Check court, date, and jurisdiction. Make sure the decision comes from a court whose authority is relevant to the question and that the answer has not mislabeled another jurisdiction’s law.
- Read the relevant passage in context. Compare the AI’s proposition with the decision’s actual language, holding, facts, and procedural posture. Do not rely on a snippet or an AI-generated paraphrase alone.
- Check subsequent history and treatment. Use a citator such as Shepard’s or KeyCite, where available, to find later references and treatment signals. Then inspect the relevant decisions and confirm what those signals mean for this issue. A positive indicator does not by itself show that a case controls or supports the exact proposition.
- Look for missing or competing authority. Search the issue independently, especially if the answer relies on a narrow set of cases or does not address an important factual or jurisdictional limit.
The Law Society of England and Wales has warned that members encountered citations that proved to be fake, documents misrepresented in an answer, and legislation from another jurisdiction incorrectly described as English and Welsh law. Its warning underscores the need to verify both the document and the way the answer uses it.
What do the published accuracy figures mean?
Published evaluations show that errors are a material risk, but their numbers describe specific tests—not a guarantee or prediction for an individual query or every current product version.
| Evaluation result | What it describes |
|---|---|
| 17% hallucination rate and 65% accuracy | Figures reported for the tested Lexis product in the Stanford evaluation, as summarized by the New York State Unified Court System Advisory Committee in its 2025 report. |
| 33% hallucination rate and 42% accuracy | Figures reported for the tested Westlaw product in the same evaluation and report. |
| 17%–33% hallucination range | The range reported by Magesh and co-authors for three tools in their 2025 benchmark. |
These results come from particular systems and query sets. They are not current universal scores for all releases, jurisdictions, or research tasks, and they do not say whether a particular answer is right. Product features and versions can change after a study.
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How should you choose or use an AI research tool?
Do not compare tools on fluent prose alone. Check what legal content your subscription covers and whether the workflow exposes a useful source trail. The following questions help assess whether a tool fits a research task:
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- Does the available collection cover the relevant jurisdiction and include the primary authority you need?
- Can you open linked primary sources and identify the passages behind material claims?
- Is a citator available, and can you inspect subsequent history and treatment?
- Can you see enough of the search and source trail to spot a false premise or an irrelevant result?
- Does the tool make jurisdictional and procedural limits clear, or could a plausible answer hide a mismatch?
- Does your practice have appropriate human review and security controls for the information you enter?
The New York State Unified Court System Advisory Committee’s 2025 report describes summer 2024 trials of AI-enhanced research platforms involving nearly 100 judges, court attorneys, law clerks, and law librarians. Participants saw possible time savings in preliminary research, including locating on-point sources and preparing first drafts, while also finding that the tools were imperfect and needed review and correction.
Thomson Reuters’ Deep Research help page puts the intended role plainly: “Use Deep Research to accelerate your research, not to replace it.” That is a sound rule for legal AI generally: let the tool help surface and organize possible authorities, but base legal reliance on your own review of the sources and their fit.
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