With this paper, we introduce RESTifAI, an LLM-driven approach for generating reusable, CI/CD ready REST API tests, following the happy-path approach. Unlike existing tools that often focus primarily on internal server errors, RESTifAI systematically constructs valid test scenarios (happy paths) and derives negative cases to verify both intended functionality (2xx responses) and robustness against invalid inputs or business-rule violations (4xx responses). The results indicate that RESTifAI performs on par with the latest LLM tools, i.e., AutoRestTest and LogiAgent, while addressing limitations related to reusability, oracle complexity, and integration. To support this, we provide common comparative results and demonstrate the tool's applicability in industrial services. For tool demonstration, please refer to this https://www.youtube.com/watch?v=2vtQo0T0Lo4. RESTifAI is publicly available at https://github.com/casablancahotelsoftware/RESTifAI.
Retrieval-Augmented Generation (RAG) is a well-established and rapidly evolving field within AI that enhances the outputs of large language models by integrating relevant information retrieved from external knowledge sources. While industry adoption of RAG is now beginning, there is a significant lack of research on its practical application in industrial contexts. To address this gap, we conducted a semistructured interview study with 13 industry practitioners to explore the current state of RAG adoption in real-world settings. Our study investigates how companies apply RAG in practice, providing (1) an overview of industry use cases, (2) a consolidated list of system requirements, (3) key challenges and lessons learned from practical experiences, and (4) an analysis of current industry evaluation methods. Our main findings show that current RAG applications are mostly limited to domain-specific QA tasks, with systems still in prototype stages; industry requirements focus primarily on data protection, security, and quality, while issues such as ethics, bias, and scalability receive less attention; data preprocessing remains a key challenge, and system evaluation is predominantly conducted by humans rather than automated methods.