Explaining Legal Terms Plainly with Generative AI

Hello, this is Legal Agent.

Contracts and statutes are full of technical language, and explaining a review result in those same terms often fails to land. Telling a business team "this clause is a representation and warranty, and a false statement here can lead to termination or damages" is not useful if the listener does not already know what a representation and warranty is. When legal explanations reach a business team still wrapped in jargon, "so what do we actually need to watch for" never gets shared, and contracts bounce back for another round of rework. Explaining a technical term in technical language is not the same as explaining it. Legal work means translating language into something the other side can act on, not just using the right words correctly.

Naming the audience produces the right level of simplicity

Telling AI who the explanation is for changes how well it lands. Adding "explain this so a middle schooler would understand" or "explain this for a business team member with no legal background" changes the simplification dramatically. Without a named audience, generative AI tends to default to a register that reads like one lawyer talking to another.

Take "surviving obligations" in an NDA (a contract promising not to disclose confidential information learned in a deal). Asked with no audience specified, AI might return something like "an obligation that continues for a set period after the contract ends," accurate, but still fairly formal. Asked to explain it "for a new employee," it comes back closer to "even after the deal is over, you still have to keep the secret for a while." Naming the audience helps organize your own thinking as much as it helps the AI.

Ask for an example, an analogy and a one-line summary together

An abstract explanation alone can be harder to follow, not easier. Asking for "a familiar example" or "a one-line summary" alongside the definition gives the listener more than one way in.

A few terms that come up often:

• Duty of care as a good manager (the standard of care a service provider owes as a professional). In practice: "as a licensed professional, take the ordinary care anyone in that role would be expected to take."
• Breach of contract (failing to do what the contract promised). In practice: "promised to do it and didn't, or missed the delivery deadline."
• Joint and several guarantee (a guarantee where the guarantor owes the full debt, on the same footing as the principal debtor). In practice: "carrying a friend's debt as if it were entirely your own."

Asking for the one-line meaning, a familiar analogy and a practical caution together tends to produce something ready to drop straight into a note for a business team.

Sample: plain-language glossary (basic form)

A basic form for explaining a set of terms in a fixed structure. Paste it in and swap the terms.

Explain the following legal terms for a business team member with no legal background. For each term, give (1) a one-line meaning, (2) a familiar analogy, and (3) a practical caution. If a technical term is unavoidable, add a short note in parentheses. Terms: representation and warranty / duty of care as a good manager / breach of contract

Sample: pulling terms from a contract into a glossary (applied form)

An applied form for feeding in an actual contract and pulling out the terms it uses as a glossary. A good starting point for something shared internally.

Extract the technical terms used in the following contract and present them as a glossary in table form: Term / Plain meaning / How it is used in this contract / Caution.
- The reader is a business team member new to legal terms
- Do not add general commentary beyond what is written in this contract
- Where the meaning is unclear or context-dependent, mark it "needs confirmation"

[Paste the contract here, remove company names and personal names]

Adding "explain only within what this contract actually says" helps keep AI from reaching for general commentary. This also gives a usable first draft of a glossary.

Common mistakes

A few mistakes worth flagging. The first is mistaking a plain-language explanation for a formal definition. Even after AI simplifies a term, the underlying rule may have changed since, and a simplified explanation is a good entry point but not a substitute for checking the actual statute or a reliable source.

The second is asking about a term without giving context. "Acceptance" means something different in a software development contract than in a goods sale, and asking without the contract type or your company's position (client or vendor) tends to return a technically correct but poorly fitted answer.

The third is oversimplifying to the point that the legal substance disappears. Explaining "exemption from liability" as simply "not being responsible" drops the part that actually matters: under what scope, and in what situation. When simplifying for a business team, check that the conditions that actually affect the decision are still there.

Building an internal glossary and FAQ

Keeping this simplification work only for the moment leaves value on the table. Turning it into an internal glossary or FAQ pays off over time. The same questions about term meaning tend to recur, and having the plain meaning and your company's own practice already written down saves repeating the same explanation. A workable split is having AI draft the glossary from past exchanges and having legal review and finalize it.

A well-built glossary also raises the baseline for how business teams read contracts, not just cutting legal's explanation load, but raising contract literacy across the company.

Things to watch for

• A plain explanation is a useful entry point, but where accuracy matters, confirm the definition against the statute or a reliable source
• A term's meaning shifts with contract type and context. Do not apply AI's general explanation directly; check it against how the term is actually used in this contract
• When pasting in an actual contract for AI to explain, watch confidential and personal information, and redact company names, personal names and amounts as needed
• Treat AI's output as a draft. A person should always give it a final check before it goes to anyone inside or outside the company