Non-deterministic output variability

Large language models can produce slightly different outputs for the same input even at the same configuration. For a regulatory document, this variability can cause two near-identical runs to read differently — even when the underlying facts are the same.

Hazardous situation: Regulatory documentation becomes inconsistent across runs because AI produces materially different outputs for the same inputs.

• Structured inputs and structured outputs. Where it matters most, Flinn's AI features operate on structured extraction tables rather than free text. See the AI Extraction Guide and Appraisal configuration. This reduces the room for stylistic drift.
• Reference-backed text. When AI generates report sections, every paragraph carries reference numbers — see AI Clinical Report Writing. Two runs may use different wording but must point to the same evidence.
• Validated against benchmarks. AI behaviour is validated before each release; see How is Flinn conducting software validation?.
• User finalisation. AI outputs are drafts to be reviewed and edited; the user remains the author of the final document. See also Misinterpretation of data for guidance on confirming AI output.
• Report unexpected variance. If two runs produce contradictory content for the same input, Report a problem or a bug — this is a signal we want to investigate.

The combination of structured inputs, reference-backed output and explicit user review keeps the residual variability within acceptable limits for regulatory writing.