Fuzz testing is one of the popular techniques for evaluating software reliability. Its effectiveness largely depends on the quality and diversity of the initial seed inputs. Traditionally, these seeds are generated randomly, which may limit the effectiveness of the fuzzing process. However, generating seeds based on an analysis of the target code can significantly improve the performance of these tools. To address this, we proposed a Large Language Model (LLM)-based seed generation approach for effective fuzzing and named it gptPromptFuzz. In our approach, initially, one meta-prompt is designed in accordance with the objective of diverse seed generation. To further enhance diversity, we construct ten additional prompts that are semantically equivalent to the meta-prompt. Each of these prompts is independently processed by LLM to produce unique seeds. The experimental results demonstrated that the proposed gptPromptFuzz outperformed random AFL in generating seeds effectively, with reduced execution time, in all 45 benchmark C programs. Further, a larger number of paths are obtained in 41 out of 45 programs.