Direct answer
Bounded divergence is an AI-output development method that allows creative variation only inside agreed limits. Source truth, proof standards, claims, audience and exclusions stay fixed; structure, examples, tone or format may vary when they are explicitly authorised.
Why this matters
This matters because creative variation only helps when the source truth stays intact. Constraints improve AI output quality by making clear what may change and what must not drift.
How to use it
- Lock the source truth before generation.
- Name fixed elements: claims, facts, audience, exclusions and proof standard.
- Name transformable elements: format, length, examples, style and order.
- Generate options inside those boundaries.
- Loop-review for drift, unsupported claims and lost intent.
Example
A knowledge-pack article may allow a shorter X post, a full essay and a checklist. It should not allow the AI to invent statistics, change the stance or remove caveats.
Caveats and limits
Bounded divergence does not mean sterile output. It means controlled variation around a stable source of truth.