Generative AI (GenAI) models, combined with Retrieval-Augmented Generation (RAG) techniques, offer a transformative approach to automating the creation of test cases from requirement documents. This solution addresses the challenges of manually interpreting and converting requirements into test scenarios and scripts. By leveraging RAG methods, users can input large or multiple documents, even those exceeding token limits, to generate test cases in formats such as scripts, Gherkin, or tabular structures.
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Introduction:
AI has moved from experimentation to a powerful engine driving organizational and business transformation. Yet many enterprises are still struggling to operationalize AI at scale. Barriers such as data privacy and sovereignty, infrastructure readiness, GPU scarcity, operational complexity and spiraling TCO continue to slow down production‑grade deployments.
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