Evidence-bounded generation
Drafting support should be constrained by identified sources and should expose where the available evidence is incomplete or ambiguous.
Responsible AI
Language capability is useful only when the evidence and decision boundary remain visible.
Generic model output can summarize and draft, but consequential licensing work also needs source retrieval, citation traceability, status context, uncertainty, and qualified review.
Citation and source inspection
A citation is a path to verification, not proof that an interpretation is correct. Material outputs should keep the underlying source within reach.
Source-status awareness
Document type, review stage, docket context, supersession, outcome, and other source-status signals can materially affect how precedent is used.
Confidence and uncertainty
Outputs should make evidentiary limits, conflicting sources, missing support, and interpretive uncertainty visible instead of smoothing them away.
Unsupported-output handling
When support is absent or unclear, the appropriate behavior is to flag the gap, request more evidence, or decline to state a confident conclusion.
Human accountability
Qualified professionals remain responsible for source validation, judgment, approval, and any consequential licensing, legal, engineering, or safety decision.
Appropriate boundary
Use outputs to support investigation and review.
Appropriate use
Source discovery, comparison, gap identification, traceability, cited drafting support, and preparation of material for qualified review.
Inappropriate use
Treating output as verified fact without source inspection, replacing qualified legal or engineering judgment, making autonomous safety decisions, or assuming NRC acceptance.
Known limitations
Sources may be incomplete, outdated, superseded, ambiguous, or contextually different. Models can produce unsupported or misleading output. Citation accuracy and NRC coverage are not guaranteed.
A relevant next step
Discuss the review controls your workflow requires.
Focus the conversation on evidence boundaries, source inspection, uncertainty, and human approval—not autonomous decision-making.