A generic chatbot answers from a statistical average of the open internet. For a lot of tasks that’s fine. For legal work it’s a problem, because the answer that sounds most plausible is not necessarily the one grounded in your firm’s precedents, your jurisdiction, or your client’s actual file.
Grounding means retrieval first
A grounded system retrieves the relevant passages from a trusted corpus first, then generates an answer constrained to what it found. The corpus is your material — playbooks, prior matters, clause banks — not the public web. The model’s job shifts from “recall something plausible” to “summarize these specific sources.”
Citations are the dividing line
Because a grounded answer is built from retrieved passages, it can point back at them. Every claim carries a numbered citation a lawyer can open and verify. A generic answer can’t do this honestly — there is no underlying source to cite, only the model’s training. For legal work, an answer you can’t trace is an answer you can’t use.
- Answers reflect your firm’s best work, not the internet’s average.
- Every claim traces to a source passage you can open.
- Your privileged materials never train a public model.
Grounding and governance are different jobs
Grounding makes an answer accurate to your materials. The two-lane guard decides whether that answer is safe to ship. A serious legal AI needs both — retrieval for correctness, governance for the advice boundary.
Information and workflow assistance — not legal advice. Does not create an attorney–client relationship.
See this applied to your firm.
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