Imagine struggling with a tricky question, but instead of giving up or resorting to a search engine, you receive a helpful nudge – a hint. That's the promise of automatic hint generation using large language models (LLMs), an area explored by researchers who recently introduced a novel dataset called WIKIHINT. This dataset consists of thousands of manually crafted hints for various questions, offering a valuable resource for training and evaluating LLMs in this unique task. The researchers found that, surprisingly, shorter hints tend to be more effective, and that providing the answer to the LLM alongside the question actually helps it generate more targeted and helpful hints. This "answer-aware" approach proves especially useful in educational settings where teachers can use AI to prepare learning materials. While powerful models like GPT-4 excel in generating high-quality hints, even smaller open-source models show promise when fine-tuned on WIKIHINT. A key innovation of this work is HINTRANK, a lightweight evaluation method that uses BERT to efficiently rank hints based on their helpfulness. This method simplifies the evaluation process, making it faster and more accessible compared to traditional methods. The ability of LLMs to generate targeted hints opens up exciting possibilities for education, interactive learning, and problem-solving. Imagine personalized hints tailored to your specific learning style and knowledge gaps. However, challenges remain, such as efficient user profiling and navigating the computational demands of LLMs. As this research unfolds, we're moving closer to a future where AI can act not just as an answer machine, but as a personalized guide, gently nudging us towards discovery and understanding.