Large Vision-Language Models (LVLMs) are revolutionizing how we interact with images, but they sometimes 'hallucinate,' generating descriptions that don’t match the image. This happens because the language model part of the system sometimes overpowers the visual input, a phenomenon researchers call 'text inertia.' Imagine showing an AI a picture of a cat and it describes a dog because it previously talked about dogs—that’s text inertia. Researchers have developed a clever, training-free method called 'Pay Attention to Image' (PAI) to combat this. PAI works by boosting the attention given to image elements during the AI's processing, similar to how we might focus harder on a picture when trying to describe it. At the same time, PAI filters out predictions based solely on prior text, preventing the language model from running wild. This helps the AI focus on the actual image, leading to more accurate and less hallucinatory descriptions. While still in early stages, PAI represents an exciting step towards more grounded and reliable visual AI. This technology could dramatically enhance everything from assistive technologies for the visually impaired to more realistic and immersive virtual worlds. As AI models evolve, solving the hallucination problem will unlock their true potential and bridge the gap between human and machine perception.