NEW! CSV 105: Generative Artificial Intelligence (GenAI) for QA Professionals (one-day, in-person)
Thursday, 16 April 2026, 8:00 AM - 5:00 PM EDT
Registration Rates
- By 26 January 2026: Member $675, Non-member $845, University/Government $515*, Student/Outreach Members $350
- 27 January - 9 March 2026: Member $720, Non-member $890, University/Government $550*, Student/Outreach Members $375
- After 9 March 2026: Member $765, Non-member $935, University/Government $585*, Student/Outreach Members $400
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RQAP re-registration units: 8.0 GCP | GLP | GMP
*Note: Participants must attend 100% of the training course to receive a certificate of attendance documenting RQAP re-registration units.
Presenters
Ricardo Torres-Rivera, Marc Altres, Santosh Tharkude, Maria Keller, Ashley Moore, Attrayee Chakraborty
Description
Module I: Introduction – Evolving QA Skillsets in the Age of Generative AI
As generative AI (GenAI) tools, such as ChatGPT, Microsoft Copilot, and Google Gemini, reshape the modern workplace, Quality Assurance (QA) Professionals (QAP) face a critical turning point. Will QAP be replaced—or will we evolve? This module aims to equip QAP with awareness, mindset, and practical strategies to augment their skillsets with GenAI, enabling them to stay relevant, boost productivity, and provide enhanced value in an AI-driven era.
Module II: The Current Landscape of Generative AI Adoption in Regulated Industry
This module provides an overview of how Generative AI is currently being explored and adopted across regulated sectors such as pharmaceuticals, biotechnology, medical devices, and contract research organizations. Presenters will share the evolving regulatory landscape, including the EU AI Act, FDA’s position and draft guidances, Annex 22, EMA reflections, and similar international initiatives shaping early adoption strategies.
Module III: Core Generative AI Skills (Human-AI Collaboration)
This module explores the following key skills Quality Assurance professionals need to work effectively with Generative AI as part of a Human–AI team:
Skill Areas
- Prompt Engineering: Crafting clear, context-rich, and constrained instructions to guide GenAI outputs. Includes iterative refinement (“prompt chaining”) and evaluation of AI responses.
- Context Framing & Guard-railing: Defining the boundaries of a problem—what data the AI should or should not use, and the compliance context must respect.
- Data Aggregation & Analysis (AI-Assisted): Combining multiple structured/unstructured data sources (e.g., audit reports, deviations, supplier forms) for synthesis. The person validates that aggregation is complete and contextually correct.
- Critical Evaluation of AI Outputs: Assessing accuracy, completeness, and compliance relevance of GenAI-generated content. Recognizing “hallucinations,” bias, or data integrity issues.
- GenAI Workflow Design: Structuring end-to-end processes that use AI responsibly. For example, a workflow consisting of inputs, model prompts, verification, and approvals.
- Visualization & Communication of AI Insights: Presenting AI findings clearly and traceably (e.g., tables, risk matrices, CAPA dashboards).
- Collaboration & Co-Creation with AI: Treating AI as a productivity partner rather than a replacement. Involves dialogic prompting (asking, verifying, refining).
This module may also include practice exercise for participants to have hands-on experience of the concepts and content shared.
Module IV: Potential Applications (Use Cases) of Generative AI Across GxP and QA Functions
Building up on the core generative AI skills (Human-AI Collaboration) shared previously, in this module presenters will share potential use-cases applications of Generative AI that can enhance quality and compliance activities across GxP domains. Use cases to be presented will show but not limited to how GenAI can support documentation review, supplier evaluation, deviation and CAPA analysis, audit preparation, risk assessment, and change control evaluation. The session emphasizes how these use-cases can streamline QA workflows, reduce manual effort, and improve consistency while maintaining human oversight and regulatory integrity. Examples are aimed to demonstrate how AI-assisted tools can transform traditional QA tasks into data-driven, proactive processes.
Module V: Risk-Based Audits of GenAI Systems
Given the dynamic nature of GenAI, traditional audit frameworks often fall short, and therefore, out-of-the-box thinking is essential. Auditors must adopt adaptive techniques such as scenario and trend analysis, adversarial testing, and cross-disciplinary collaboration to uncover hidden risks and ensure robust governance. Moreover, emerging regulations, such as the EU AI Act, EudraLex Annex 22 and evolving U.S. guidelines, demand proactive compliance strategies that integrate technical, legal, and ethical perspectives. By combining risk-based principles with innovative audit practices, organizations can not only safeguard against operational and reputational threats but also foster trust and accountability in their AI-ML deployments. This presentation will outline the imperative for a risk-sensing audit paradigm that balances technological advancement with responsible oversight.
Module VI: Ethics, Accountability, and Responsible AI Use in Regulated Environments
This module addresses the ethical principles and accountability expectations guiding the use of Generative AI within regulated life science organizations. Participants will explore topics such as data privacy, confidentiality, bias mitigation, transparency, and traceability of AI-assisted decisions. The discussion will highlight how ethical considerations intersect with regulatory requirements, emphasizing the QA professional’s role in ensuring trustworthy and compliant AI practices. Practical examples will illustrate how to implement responsible-use guidelines, maintain human oversight, and document AI involvement in quality and compliance workflows.
Module VII: Group Exercise Challenge: Streamlining a QA Process with Generative AI
In this capstone module, participants will apply the concepts and tools explored throughout the day in a practical, team-based challenge. Working in small groups, they will analyze a real-world QA problem and design a Generative AI-enabled solution through structured critical thinking and collaborative brainstorming. Each team will define its strategic approach, identify data inputs, craft effective prompts, and then implement and refine the solution using a Generative AI tool such as ChatGPT. Multiple iterations will allow participants to evaluate and improve their designs, exploring additional opportunities to enhance the proposed AI-assisted workflow. The module concludes with team presentations highlighting their prototype design, Human-AI collaboration experience, and key lessons learned about productivity, creativity, and responsible AI integration in QA processes.
Module VIII: Wrap-Up and Key Takeaways
Guided reflection on the key insights gained throughout the day. The session reinforces practical actions attendees can take to begin piloting or supporting GenAI initiatives within their organizations. Participants will also share final reflections on how Human–AI collaboration can strengthen quality culture and innovation.
Objectives
At the end of the course, the participants shall be able to:
- Explain the foundational concepts of Generative AI and differentiate between productivity-enhancing and regulated applications within GxP environments.
- Identify key Human–AI collaboration skills—such as prompting, data aggregation, workflow design, and critical output evaluation—to common QA functions.
- Discuss how Generative AI can streamline QA activities through use cases like documentation review, supplier evaluation, risk assessment, deviation analysis, and audit preparation.
- Develop and apply a risk-based and ethical framework for assessing, auditing, and governing Generative AI tools in alignment with emerging regulatory expectations (e.g., EU AI Act, FDA, EMA)
- Design and present a Generative AI–enabled QA workflow through collaborative group exercises, demonstrating strategic thinking, iterative prompting, and responsible AI use.
Target Audience
The course is oriented to all QA Professionals across the multiple QA disciplines (GLP, GMP, GCP, Medical Devices, CSV, Gene Cell Therapies, CDMO's, etc.)
Agenda
- 8:00AM - 9:00AM - Module I: Introduction – Evolving QA Skillsets in the Age of Generative AI
- 9:00AM - 10:00AM Module II: The Current Landscape of Generative AI Adoption in Regulated Industry
- 10:00AM - 11:00AM Module III: Core Generative AI Skills (Human-AI Collaboration)
- 11:00AM - 12:00PM Module IV: Potential Applications (Use Cases) of Generative AI Across GxP and QA Functions
- 1:00PM - 1:45PM - Module V: Risk-Based Audits of GenAI Systems
- 1:45PM - 2:30PM - Module VI: Ethics, Accountability, and Responsible AI Use in Regulated Environments
- 2:30 PM - 4:30PM Module VII: Group Exercise Challenge: Streamlining a QA Process with Generative AI
- 4:30PM - 5:00PM Module VIII: Wrap-Up and Key Takeaways
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