Large Language Models (LLMs) have revolutionized how we interact with technology, displaying incredible fluency in generating human-like text. However, when it comes to complex reasoning tasks, LLMs can sometimes falter. A technique called "in-context learning" helps improve LLM reasoning by providing examples within the prompt itself. But simply throwing any examples at the model isn't enough. The real magic lies in carefully choosing the *right* examples. Researchers have explored various methods for selecting these examples, often focusing on uncertainty – picking examples the model is least sure about. But what if there's a better way? A new study introduces "Adaptive Prompting," a smarter approach to in-context learning. Instead of choosing all the examples upfront, Adaptive Prompting selects them one by one, taking into account the model's feedback from *previous* examples. This iterative process helps avoid redundant information and ensures that each new example adds genuine value, teaching the model to reason more effectively. Think of it like a personalized tutoring session: the tutor (Adaptive Prompting) presents a problem, assesses the student's (LLM's) understanding, and then tailors the next problem based on the student's response. This tailored approach leads to more efficient learning. The researchers tested Adaptive Prompting on various reasoning tasks, including math problems, commonsense questions, and symbolic reasoning puzzles. The results are promising, with Adaptive Prompting consistently outperforming traditional methods. While LLMs are rapidly evolving, techniques like Adaptive Prompting are essential to unlock their full reasoning potential, paving the way for even more intelligent and capable AI systems. The research suggests Adaptive Prompting is particularly beneficial for less powerful LLMs, boosting their performance significantly. This makes the technique valuable for a wide range of applications, making advanced AI capabilities accessible to a broader audience. The ability to refine LLM reasoning through strategic example selection opens up exciting possibilities for the future of AI, including improved problem-solving, more accurate question answering, and a deeper understanding of complex information.