Imagine a world where access to information isn't limited by language. That's the vision driving advancements in Natural Language Processing (NLP), particularly for languages like Hindi, spoken by millions worldwide. A major hurdle in developing truly intelligent AI for Hindi has been the lack of robust benchmarks to evaluate how well these systems understand and respond to complex queries. Existing methods often rely on translated English datasets, which introduce biases and inaccuracies, hindering progress. Enter "Suvach," a groundbreaking new benchmark designed specifically for evaluating Hindi question-answering models. Instead of relying on translations, Suvach leverages the power of large language models (LLMs) to generate a high-quality dataset directly in Hindi. This innovative approach ensures the benchmark accurately reflects the nuances of the language, providing a more reliable evaluation tool. Suvach focuses on 'extractive question answering,' where the AI needs to find the answer within a given text passage, much like we do when researching. This benchmark opens doors to developing more sophisticated Hindi NLP models, capable of understanding context, extracting relevant information, and answering complex questions accurately. The implications are far-reaching, from improved search engines and educational tools to more effective customer service and healthcare applications. While Suvach represents a significant leap forward, the journey doesn't end here. The research highlights the need for continued exploration of LLM-powered benchmark creation for various tasks and across a broader range of Indic languages. This is just the beginning of a revolution in how we access and interact with information in languages beyond English, paving the way for a more inclusive and accessible digital future.