Large language models (LLMs) are revolutionizing how we interact with technology. They can write stories, answer questions, and even generate code. But their sheer size makes them computationally expensive and slow. What if there was a way to get similar performance from a smaller, faster model? Researchers have explored a novel approach called Guidance-based Knowledge Transfer (GKT), which uses a larger LLM as a "teacher" to guide a smaller "student" LLM. Think of it like a teacher giving a student a helpful nudge in the right direction. The teacher LLM generates short prompts based on user input, and the student LLM uses these prompts to generate the final response. This method eliminates the need for computationally intensive fine-tuning of the student model. Experiments show that GKT significantly boosts the accuracy and speed of smaller LLMs. For instance, using a powerful LLM like ChatGPT as the teacher, a smaller model can achieve nearly 95% of ChatGPT's performance at a fraction of the cost. This approach is particularly well-suited for cloud-edge computing, where smaller models can be deployed on edge devices like smartphones, reducing latency and data transmission costs. The teacher model resides in the cloud, providing guidance to multiple student models simultaneously. This allows for personalized user experiences while maximizing efficiency. While GKT shows great promise, challenges remain. Determining the optimal length of the guidance prompts is crucial. Too short, and the student might get lost; too long, and the benefits of using a smaller model diminish. Future research will likely focus on refining these guidance strategies and exploring how GKT can be applied to a wider range of tasks. GKT represents a significant step towards making LLMs more accessible and efficient, paving the way for a future where powerful AI capabilities are available on even the most resource-constrained devices.