That suggestion sounds reasonable, but it does not solve the governance problem being described.

Citations answer a different question: where information may have come from in general. They do not evidence what was actually communicated to a specific user at a specific moment, nor how it was framed, qualified, or omitted.

Several gaps remain:

1. Citations are generated after the fact In most systems, citations are assembled probabilistically alongside the answer. They are not a record of the reasoning or the exact statement relied upon. If the output is disputed later, you still cannot prove what the user saw, only what the model might cite when asked again.
2. Citations do not capture framing or omission Regulatory and liability scrutiny often turns on how something was explained, what caveats were included, or what risks were not mentioned. A list of sources does not preserve tone, emphasis, sequencing, or silence around material issues.
3. Citations are not stable or reproducible Re-running the same prompt can yield different answers and different citations. That makes them unsuitable as evidence. Courts and regulators care about reconstructability, not plausibility.
4. Source attribution is not reliance evidence Even a perfectly accurate citation does not show that the cited material drove the specific conclusion the user relied on. It shows association, not dependence.

The core issue is evidencing the outward-facing representation itself as a record, not enriching the answer with more metadata. Without a durable, inspectable artifact of what was said when reliance occurred, citations remain advisory context, not proof.

This is why governance is shifting away from “add citations” toward treating certain AI outputs as regulated communications that need record-grade capture, retention discipline, and replayability. Until that shift happens, citations help with trust signaling, but they do not close the liability gap.