The increasing complexity and variability of contextual inputs in artificial intelligence applications necessitate models capable of dynamic adaptation without external intervention. Introducing the Context-Driven Self-Adaptation Mechanism (CDSAM), a novel framework that enables large language models (LLMs) to autonomously adjust their generative processes in response to evolving contextual cues. By integrating dynamic context modules with the model's learning layers, CDSAM facilitates real-time modulation of outputs, enhancing adaptability and responsiveness. Empirical evaluations demonstrate significant improvements in context alignment and generative coherence, showing the potential of CDSAM to advance the development of more intelligent and responsive AI systems.