Navigating the Shift: Mastering QA for RAG and AI Systems

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Navigating the Shift: Mastering QA for RAG and AI Systems

As Generative AI moves from experimental labs to production environments, the traditional QA playbook is being rewritten.

This meetup is designed for technical testers and engineers who are moving beyond manual checks into the world of non-deterministic testing.

We’ll dive deep into the mechanics of RAG systems, identify where these complex pipelines typically fail, and establish a framework for measuring "good" AI outputs.

Whether you are grappling with hallucinations or trying to define reliability metrics, this session provides the blueprints you need to ensure your AI is production-ready.

What You’ll Learn:

• The RAG Architecture: A QA-centric breakdown of Retrieval vs. Generation and where the most common failures hide.
• Testing Non-Deterministic Outputs: Strategies for threshold-based validation and handling varied responses.
• AI Performance Metrics: How to measure accuracy, relevance, and fluency effectively.
• Mitigating Hallucinations: Practical techniques to identify fabricated responses using benchmark datasets.
• Consistency & Bias: Frameworks for ensuring stable, ethical, and safe AI outputs across multiple runs.

Agenda:

• Welcome & Introductions
• Session 1: Testing RAG Systems: A QA Approach to Reliable AI
• Session 2: AI Metrics Evaluation as a QA
• Live Q&A: Troubleshooting your AI testing hurdles
• Lunch & Networking: Lunch and conversations

Who Should Attend:

• QA Engineers & SDETs
• Automation Engineers
• AI/ML Engineers
• Engineering Leaders & Product Managers

Join us to bridge the gap between traditional quality assurance and the future of AI reliability.

[Reserve your spot today]