Large language models (LLMs) possess extensive prior knowledge and powerful in-context learning (ICL) capabilities, presenting significant opportunities for low-resource tasks. Though effective, several key issues still have not been well-addressed when focusing on zero-shot named entity recognition (NER), including the misalignment between model and human definitions of entity types, and confusion of similar types. This paper proposes an Evolving Prompts framework that guides the model to better address these issues through continuous prompt refinement. Specifically, we leverage the model to summarize the definition of each entity type and the distinctions between similar types (i.e., entity type guidelines). An iterative process is introduced to continually adjust and improve these guidelines. Additionally, since high-quality demonstrations are crucial for effective learning yet challenging to obtain in zero-shot scenarios, we design a strategy motivated by self-consistency and prototype learning to extract reliable and diverse pseudo samples from the model’s predictions. Experiments on four benchmarks demonstrate the effectiveness of our framework, showing consistent performance improvements.