Large Language Models (LLMs) are impressive, but they struggle when faced with complex reasoning involving multiple sources, such as text and images. Imagine an AI trying to analyze a video, needing to connect the visuals with the spoken words. It's a tough task that requires more than just recognizing individual elements – it requires true understanding of relationships. Researchers have developed an interactive system called POEM to help LLMs reason more effectively with multimodal input. POEM acts as a guide, helping users craft the best prompts by providing tools to explore interaction patterns between text and image, offering recommendations based on effective examples, and allowing users to inject their own expert knowledge into the process. This human-in-the-loop approach makes AI more transparent and controllable. Instead of just throwing data at a model, POEM empowers users to understand *why* and *how* decisions are made, creating opportunities for continuous improvement. The system uses different levels of analysis. It looks globally at model performance on a task, then zooms in to examine specific groups or even individual examples, enabling users to pinpoint problems and propose solutions. If the model misinterprets a smiling face in a video, POEM makes it easy to see the error and suggest modifications to the prompt, such as, "Pay attention to the words people use to express their emotions, even if their facial expressions seem to contradict them." The research demonstrated the power of POEM in tasks such as sentiment analysis and predicting user intent. By combining human expertise with AI's processing power, they significantly improved performance and paved the way for more robust, reliable multimodal reasoning in the future. The challenge ahead lies in scaling this approach and making it work seamlessly across various applications. But with the innovative POEM system, we’re a step closer to building truly intelligent multimodal AI.