Temporal Prompt Engineering for Generative Semantic Communication

The rapidly evolving field of generative artificial intelligence technology has introduced innovative approaches for developing semantic communication (SemCom) frameworks, leading to the emergence of a new paradigm—generative AI-assisted SemCom (GSC). Benefiting from its strong ability to understand and generate high-quality content across various domains, this approach can effectively address the reconstruction limitations that challenge traditional SemCom systems. However, this architecture often suffers from high latency due to the complex processes involved in semantic extraction and generative semantic inference. To mitigate this issue, we propose a low-latency GSC framework, achieved by enabling the parallel execution of the transmitter’s semantic extracting and the receiver’s generating processes from a macro perspective. Furthermore, to attain more accurate and semantically aligned reconstruction, we design a temporal prompt engineering approach that utilizes reinforcement learning to sequence the temporal feature extraction steps at the transmitter. The results show that compared to the conventional GSC architecture, our designed framework can achieve a 52% reduction in residual task latency that extends beyond the fixed inference duration while only incurring an approximate 9% decrease in task score.