Model optimization, such as pruning and quantization, has become the \emph{de facto} pre-deployment phase when deploying deep learning (DL) models in resource-constrained platforms. However, the complexity of DL models often leads to non-trivial bugs in model optimizers, known as \emph{model optimization bugs} (MOBs). These MOBs are characterized by involving complex data types and layer structures inherent to DL models, causing significant hurdles in detecting them through traditional static analysis and dynamic testing. In this work, we leverage Large Language Models (LLMs) with prompting techniques to generate test cases for MOB detection. We explore how LLMs can draw an understanding of the MOB domain from scattered instances and generalize to detect new ones, a paradigm we term as \emph{concentration and diffusion}. We extract MOB domain knowledge from the artifacts of known MOBs, such as their issue reports and fixes, and design knowledge-aware prompts to guide LLMs in generating effective test cases. The domain knowledge of code structure and error description provides precise in-depth depictions of the problem domain, i.e., the \emph{concentration}, and heuristic directions to generate innovative test cases, i.e., the \emph{diffusion}. Our approach is implemented as a tool named YanHui and benchmarked against existing few-shot LLM-based fuzzing techniques. Test cases generated by YanHui demonstrate enhanced capability to find relevant API and data combinations for exposing MOBs, leading to an 11.4% increase in generating syntactically valid code and a 22.3% increase in generating on-target code specific to model optimization. YanHui detects 17 MOBs, and among them, five are deep MOBs that are difficult to reveal without our prompting technique.