Abstract: Software testing is going through a big transformation with the adoption of Generative AI. We can clearly see that large language models are very good at things like test case generation, defect analysis, and helping in automation activities. But one important limitation which we are seeing is that these models do not have access to our system-specific and organization-specific context. Because of this, most of the time the output which we are getting is very generic. It does not really reflect how our real application works, what kind of historical defects we had, what are our framework standards, and what kind of domain knowledge is required. So even though the model is powerful, the lack of real context is becoming a bottleneck. In this session, I am introducing Retrieval-Augmented Generation (RAG) as a very practical and scalable approach to build context-aware testing systems. Instead of using only the large language model, we are combining it with our enterprise knowledge sources. This includes things like requirements documents, historical test cases, automation repositories, execution logs, defect databases, and architectural documentation. By doing this, RAG helps the testing tools to generate outputs which are more relevant, more accurate, and more aligned with how our organization actually works. It is no longer just a generic AI answer. It becomes an answer which understands our application, our data, and our testing practices. In this talk, I will be covering real-world use cases of RAG across the testing lifecycle. This includes requirement-to-test case generation, self-healing automation, intelligent failure analysis, risk-based test prioritization, and QA knowledge assistants. These are practical scenarios where RAG can directly add value to day-to-day testing activities. I will also explain how to design RAG pipelines, how to select the right data sources, how to implement vector-based retrieval, and how to integrate RAG into existing QA frameworks and CI/CD pipelines. The idea is to make this not just theoretical, but something which can be applied in real projects. Through architecture patterns, implementation strategies, and practical lessons learned, this session will show how RAG can move software testing from static and rule-based processes to more intelligent, adaptive, and context-driven quality engineering systems. By the end of the session, participants will have clear and actionable guidance to start building RAG-powered testing solutions within their own organizations.