When interacting with the world robots face a number of difficult questions, having to make decisions when given under-specified tasks where they need to make choices, often without clearly defined right and wrong answers. Humans, on the other hand, can often rely on their knowledge and experience to fill in the gaps. For example, the simple task of organizing newly bought produce into the fridge involves deciding where to put each thing individually, how to arrange them together meaningfully, e.g. putting related things together, all while there is no clear right and wrong way to accomplish this task. We could encode all this information on how to do such things explicitly into the robots' knowledge base, but this can quickly become overwhelming, considering the number of potential tasks and circumstances the robot could encounter. However, images of the real world often implicitly encode answers to such questions and can show which configurations of objects are meaningful or are usually used by humans. An image of a full fridge can give a lot of information about how things are usually arranged in relation to each other and the full fridge at large. Modern generative systems are capable of generating plausible images of the real world and can be conditioned on the environment in which the robot operates. Here we investigate the idea of using the implicit knowledge about the world of modern generative AI systems given by their ability to generate convincing images of the real world to solve under-specified tasks.