Imagine telling a robot to grab a screwdriver, not with complex code, but simply by saying, "Pick up the screwdriver, handle first." This is the exciting potential of language-guided dexterous grasping, a field that's transforming how robots interact with the world. Researchers are tackling the challenge of teaching robots to understand natural language commands and translate them into precise hand movements. A major hurdle has been the lack of datasets that combine human language with detailed grasp information. To overcome this, a new dataset called DexGYSNet has been created. It uses a clever system to translate human hand movements into robotic ones and then uses a large language model (LLM) to automatically generate descriptions of these grasps. This dataset powers a new framework called DexGYSGrasp, which allows robots to generate a variety of grasps based on language instructions. The framework works in two stages: first, it learns to generate grasps that align with the user's intent, and then it refines these grasps to ensure they are stable and avoid collisions. This two-stage approach is key to overcoming the challenge of balancing grasp quality with the user's intention. The results are impressive, with robots successfully grasping a range of objects based on diverse language commands. While still in its early stages, this research opens doors to a future where robots can seamlessly integrate into our lives, assisting with everyday tasks through simple, intuitive communication. Challenges remain in transferring these skills to real-world scenarios, but the progress so far is a significant step towards more natural and effective human-robot collaboration.