Imagine asking an AI to draw a picture of a cat wearing a top hat, only to receive an image of a dog in a bowler hat. This isn’t a hypothetical scenario; it’s a real problem with AI image generators, often referred to as “hallucinations.” These hallucinations, where the generated image doesn't match the text description, stem from inconsistencies between the AI's understanding and its visual output. Researchers are tackling this challenge head-on, and a new framework called Prompt-Consistency Image Generation (PCIG) offers a promising solution. PCIG uses a clever combination of Large Language Models (LLMs), knowledge graphs, and controllable diffusion models to improve the accuracy of AI-generated images. It works by first extracting objects and their relationships from the text prompt, then uses this information to guide the image generation process. Think of it like giving the AI a blueprint before it starts painting. The knowledge graph acts as a map of the image, telling the AI where each object should be placed. The results are impressive: PCIG significantly reduces hallucinations, creating images that more faithfully represent the given text. This breakthrough has exciting real-world implications, from more accurate medical imaging and enhanced criminal investigations to generating more consistent visual content in creative fields. While still in development, PCIG represents a significant leap forward in tackling the hallucination problem in AI image generation. As AI models become increasingly sophisticated, solutions like PCIG pave the way for more reliable and creative applications of this transformative technology.