Source-grounded generation is a requirement for legal and finance
In legal and finance, an AI failure is not a mere wrong answer — it becomes an accountability problem. That is why the requirement in these domains is not about the output itself, but output whose basis can be shown.
Source-grounded generation is more than attaching citations. Claims and evidence must be connected without delay, and the original text must be openable. When reviewing a contract clause, the statutes and judgments reflected in it are shown directly in the result; when assessing risk, the data used is displayed alongside.
Implementing this requires a structure where retrieval, generation, and verification are separated. Retrieval gathers candidate evidence, generation writes the answer on top of that evidence, and verification checks the claim-to-source matching. When the match is weak, the answer is flagged as a problem. Because this pipeline pulls real documents instead of inventing sources during generation, the risk of hallucination is structurally reduced.
In practice, what matters is that the user can also open the sources. A list of references is presented beside the answer, each linking directly to the original text, the specific provision, or the case number for practical verification.
AI is entering legal and finance work cautiously but clearly. The condition that keeps that boundary trustworthy is source-grounded generation.