Quality Assurance for GenAI Outputs

Build review frameworks to validate AI-generated content.

Pillar

Process – Workflow, Governance, Risk & Efficiency

Overview

This course provides a comprehensive approach to establishing quality assurance (QA) processes for Generative AI outputs. Participants will learn to design validation frameworks, implement review mechanisms, and ensure the accuracy, relevance, and ethical compliance of AI-generated content.

Learning Objectives

Participants will be able to:

• Understand key quality challenges with GenAI outputs

• Develop effective QA frameworks tailored to AI-generated content

• Implement human and automated review processes

• Identify and mitigate bias, errors, and inconsistencies

• Maintain compliance with regulatory and ethical standards

Target Audience

• QA managers and specialists

• AI developers and data scientists

• Content creators and editors

• Compliance and risk officers

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Practical workshops on QA framework design

• Case studies highlighting QA successes and failures

• Hands-on exercises reviewing AI-generated samples

Materials Provided

• QA framework templates and checklists

• Tools and techniques for content validation

• Guidelines for ethical and regulatory compliance

• Certificate of completion

Outcomes

• Establish robust QA processes for GenAI outputs

• Improve reliability and trustworthiness of AI-generated content

• Detect and correct errors and bias effectively

• Align AI outputs with organizational and legal standards

Outline / Content

Day 1: Introduction to Quality Challenges in GenAI

• Understanding AI-generated content characteristics

• Common quality issues and risks

• Importance of QA in AI workflows

Day 2: Designing QA Frameworks

• Components of effective QA processes

• Integrating human and automated reviews

• Defining quality metrics and standards

Day 3: Implementing Review Mechanisms

• Workflow setup for content validation

• Tools and platforms for QA automation

• Training reviewers and evaluators

Day 4: Compliance and Continuous Improvement

• Ethical considerations and regulatory requirements

• Monitoring QA effectiveness

• Iterative refinement of QA frameworks

• Workshop: Develop a QA plan for a GenAI content use case