Review and evaluation of AI outputs

A fluent answer does not necessarily mean a good output. This module shows how to systematically evaluate the results of AI tools according to specific criteria, instead of relying on first impressions.

Participants will learn to assess output according to accuracy, completeness, relevance, clarity and usability, and will master the basic procedures for fact-checking and working with sources. The module shows how to recognize uncertain, misleading or overly general answers, and also addresses the topic of bias and one-sided views that can appear in AI outputs.

An important part is also orientation in when one’s own judgment is no longer enough and professional review is necessary. The module shows how to use AI to review one’s own text without becoming the sole authority, and where the limits of automated decision-making are. The participant leaves the module with a specific habit – to verify the output before passing it on to colleagues, clients or management.

• employees and managers who pass on AI outputs to colleagues, clients or management,
• companies that want to reduce the risk of erroneous or distorted outputs in processes,
• people who want to gain a systematic quality assessment procedure, not just intuition,
• participants following previous AI Academy modules.

• Quality criteria: accuracy, completeness, relevance, clarity and usability
• Fact checking and working with sources
• Recognizing uncertain, misleading or overly general answers
• Bias, distortion and one-sided views
• When expert review is needed
• Using AI to review your own text – and its limits
• Output-based decision-making and the limits of automation

• Evaluate AI outputs for accuracy, completeness, relevance, understandability, and usability.
• Verify facts and work with sources.
• Recognize uncertain, misleading, or biased responses.
• Assess when expert review is needed.
• Understand the limits of automation when making decisions based on AI outputs.

How to tell if AI output is poor quality?
According to criteria such as accuracy, completeness, relevance, and clarity – and whether the output is substantiated and verifiable.

What is bias in AI output?
It is a distortion or one-sided view that can arise based on the data the model was trained on.

When is expert advice necessary? output control?
Typically where the output influences decision-making, communication with clients or contains professional, legal or security aspects.

Can AI also be used to check your own text?
Yes, but as a supplementary tool, not as the sole authority - the result always needs to be assessed by your own judgment.

Who is the module intended for?
You who further use AI outputs in work, communication or decision-making; and want to reduce the risk of error.