Introduction
An educator asks AI for a set of examples, and the output appears confident, but one of the examples contains a subtle error. A coach uses AI to summarize a set of reflections, and the summary misses a key perspective. An administrator drafts a message with AI support, and a single inaccurate claim can cause confusion because others rely on its accuracy.
This module helps you evaluate AI outputs using a consistent routine rather than relying on intuition or fluency. As you proceed, you use an Output QA process to check accuracy, bias, and readiness. Then you revise drafts to a Ready V2 version and embed evaluation steps into templates and workflow gates so quality checks happen by default, even when people are busy.
Learning objectives
Upon completion of this module, you'll be able to:
- Identify common output risks, including inaccurate claims, missing context, and biased or narrow representation.
- Apply an Output QA routine to check accuracy, alignment, inclusivity, and readiness before use or sharing.
- Verify the highest impact claims against an authoritative source appropriate to the task.
- Revise an AI-generated draft into a Ready V2 version that is accurate, clear, and appropriate for the audience.
- Embed evaluation steps into a reusable template, including a QA checklist and a short attribution and transparency footer.