Large language models (LLMs) like GPT-3 have revolutionized how we interact with AI. But their performance often hinges on a hidden key: the prompt. A poorly crafted prompt can lead to underwhelming results, while a well-optimized one unlocks the LLM's true potential. Researchers have been exploring how to automatically select the best examples to include in prompts, a process called exemplar selection. This is crucial because the right examples can significantly boost an LLM's ability to learn and generalize to new tasks. However, current methods often fall short. Some require computationally expensive calculations for each new input, while others ignore the critical role of example order. A new research paper introduces EASE (Efficient ordering-aware Automated Selection of Exemplars), a novel approach to prompt optimization. EASE uses a clever combination of techniques to efficiently find the best set and order of examples for a given task. It leverages pre-trained language models to represent examples and employs a neural bandit algorithm to explore and identify the most effective combinations. What sets EASE apart is its ability to consider the order of examples, a factor often overlooked but crucial for optimal performance. Furthermore, EASE can be extended to optimize the instructions given to the LLM alongside the examples, creating a fully automated prompt tuning pipeline. Experiments show that EASE consistently outperforms existing methods, especially on tasks the LLM hasn't encountered before. This suggests that careful exemplar selection is even more critical when LLMs face unfamiliar territory. The research also highlights an interesting insight: as an LLM becomes more familiar with a task through fine-tuning, the impact of exemplar selection diminishes. This suggests that methods like EASE are most valuable during the initial stages of adapting an LLM to a new task. EASE offers a promising path towards unlocking the full power of LLMs by automating the often-tedious process of prompt engineering. This could lead to more efficient and effective use of LLMs across various applications, from chatbots and code generation to complex reasoning tasks. However, challenges remain, such as the computational cost of embedding exemplar sequences and the need for suitable validation data. Future research could explore more efficient embedding methods and strategies for dealing with limited validation data. As LLMs continue to evolve, so too will the methods for optimizing their performance. EASE represents a significant step forward in this ongoing quest to make AI more powerful and accessible.