
Recent advancements in artificial intelligence have sparked significant interest in optimizing Large Language Models (LLMs) to tackle real-world tasks more efficiently. A study conducted by researchers at Google DeepMind and Stanford University delves into two primary customization strategies: fine-tuning and in-context learning (ICL). This research aims to explore how these strategies can be employed to cater to specific task requirements,