28th February 2026
10:30 AM - 01:30 PM IST
28th February 2026
10:30 AM - 01:30 PM IST
Ideas to Impacts
Lane, 3, near VIJAY SALES - BANER, Pallod Farms, Baner, Pune, Maharashtra 411069
Generative AI doesn’t fail like traditional software — it drifts, hallucinates, and behaves probabilistically.
Accuracy alone is not quality; reliability, relevance, safety, and consistency must be measured.
Evals are the new test cases — structured, repeatable, and tied to real-world use cases.
Golden datasets and clear metrics turn AI from “it looks good” into “it works as expected.”
Automated evaluation pipelines bring AI into CI/CD and engineering governance.
From a Principal QE lens: if we don’t evaluate it rigorously, we shouldn’t ship it.
Veerkumar Patil is a Quality Engineering Leader at Red Hat with 13 of experience in test automation, and AI-driven quality practices. He specializes in building scalable automation frameworks across UI, API, and CLI layers using tools like Selenium, Cypress, and Playwright. Veerkumar is actively shaping QE + AI CoE initiatives, focusing on LLM evaluation frameworks, golden datasets, and AI-powered testing strategies. A Red Hat Certified Specialist in Containers, he is also an open-source contributor and community speaker who shares practical insights on modern quality engineering, automation architecture, and the evolving role of testers in the AI era.
AI systems are transforming how products make decisions, interact with users, and generate outcomes. However, unlike traditional software, AI behaves probabilistically, evolves with data, and introduces risks such as bias, drift, hallucinations, and explainability gaps. Conventional pass/fail testing models are no longer sufficient. Quality Engineering must expand beyond functional validation to evaluate model performance, fairness, robustness, safety, and ongoing reliability in production environments.
This session explores how QE leadership can build structured AI evaluation frameworks that align with governance expectations and risk principles. It presents a strategic blueprint to transform QE teams into trust enablers, ensuring AI systems are not just innovative, but responsible, measurable, and resilient at scale.”
Chetan has led initiatives spanning automation, performance, and strategic quality practices that bridge traditional QE with modern AI-enabled systems. His passion lies in evolving QE from tactical execution to strategic leadership — empowering teams to redefine quality in the age of intelligent systems. Chetan actively explores how organizations can build trust, robustness, and responsible governance into AI evaluation and assurance practices, and he regularly contributes insights on QE transformation, innovation, and AI quality leadership.
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