Evaluation of Generative AI Outputs

Course overview

This training is aimed at participants who want to professionally evaluate and improve generative AI outputs. The focus is on output quality, consistency, and refinement of results. Evaluating Generative Outputs Not every output from a generative AI system is readily usable. Therefore, participants will learn to systematically assess outputs for quality, relevance, and professional consistency. - Assess quality professionally - Check relevance and consistency - Align outputs with professional context Controlling Consistency and Fit - Compare repeatability - Recognize differences in output Targeted Refinement of Results When AI outputs do not fit, it is often due to unclear inputs or missing contextual information. Participants will learn to systematically improve weak results instead of simply accepting the output. - Improve outputs - Specify responses - Classify causes of weak results Recognizing the Limits of Generative Systems - Classify uncertainties in output - Understand critical points - Know typical weaknesses of generative models - Ensure responsible further processing Building a Professional Approach to Results - Strengthen professional evaluation - Increase reliability in use Applying Quality Assurance for Generative AI - Develop evaluation criteria - Process results responsibly - Establish own evaluation routines This course teaches how to professionally evaluate, improve, and ensure the quality of generative AI outputs for professional use.

Upcoming dates

• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-11-03T23:00:00.000Z · 2027-05-03T22:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-11-03T23:00:00.000Z · 2027-05-03T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-11-17T23:00:00.000Z · 2027-01-19T23:00:00.000Z · Frankfurt am Main · Hybrid Learning