Post-Processing (in AI)

Post-Processing (in AI)

Steps applied after a model produces output, such as filtering, formatting, or fact-checking.

"We used post-processing to prompt for human review every time the AI response had a 'fact' in it.

No items found.

Overview

Post-processing: filtering what goes

comes out of the generative AI model or tool

AI can be guided and groudned by controlling outputs before they reach the user. Post-processing can tag or flag certain types of content—for example, personally-identifiable information, copyrighted materials or hate speech. Post-processing can also highlight or flag factual claims or citations so a human or another system can verify that they are accurate (rather than hallucinations).

  • Pros: Reduces risks without retraining the model.
  • Cons: Adds overhead and may miss edge cases if filters are poorly designed.
  • Think about: Input sanitization, moderation APIs, truth-checking workflows, and layered review systems, pre-processing.

​

How to Think About

Post-Processing (in AI)

Practical Applications of

Post-Processing (in AI)