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How do I prevent repetitive outputs across runs?

Track previous outputs and provide a ‘do not repeat’ list. Ask for novel angles and enforce diversity constraints (e.g., different frameworks or audiences). For content, include a uniqueness check step where the model compares against prior items.

In practice

Seed with an example of what ‘too similar’ looks like to calibrate the model.

Where to start

  1. [01]Store prior outputs and hash them.
  2. [02]Add a novelty instruction block.
  3. [03]Run a self-comparison step pre-final.
  4. [04]Spot-check with humans monthly.
deduplicate AI outputsavoid repetition LLMnovelty prompting

Related questions

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Open source · Python