Table of Contents
Which GenAI Practice Is Not Recommended and Why Does It Matter?
Learn which GenAI practice is not recommended and why verifying truthfulness, completeness, bias, and ethical risks matters.
Question
According to this module’s second reading, which of the following practices is NOT recommended when using GenAI tools?
A. Accepting GenAI outputs at face value without verification.
B. Evaluating outputs for truthfulness, completeness, and logical consistency.
C. Applying subject matter expertise to assess output accuracy.
D. Examining outputs for ethical considerations and potential biases.
Answer
A. Accepting GenAI outputs at face value without verification.
Explanation
A central best practice when using GenAI is to verify its outputs rather than assume they are correct just because they sound confident or polished. Accepting answers at face value can lead to errors, hallucinations, biased conclusions, and poor decisions, especially in high-stakes work.
Why the others are recommended
B is recommended because truthfulness, completeness, and logical consistency are core checks for evaluating GenAI output quality. C is also recommended because subject matter expertise helps users spot inaccuracies, weak reasoning, and missing context that automated systems may miss.
D is recommended as well because ethical concerns and bias are major parts of responsible AI use. Good practice involves checking whether outputs reinforce stereotypes, omit important perspectives, or create harmful consequences.