Language models have achieved substantial advancements in generating text that approximates human-like coherence, yet challenges in maintaining contextual alignment across diverse inputs remain unresolved. Addressing these challenges through a novel mechanism, contextual-distillation introduces a structured recalibration of embeddings to mitigate issues of semantic drift and inconsistency in generative pathways. This study presents a thorough investigation into contextualdistillation's capacity to enhance embedding coherence and generative accuracy, deploying a comprehensive methodology centered on embedding recalibration across multi-layer attention structures. The experiments, conducted on a recent open-source model, reveal that contextual-distillation significantly reduces entropy in attention distributions, aligns embeddings with intended semantic cues, and improves coherence metrics across complex input types. Quantitative results demonstrate that the proposed mechanism enables refined pathway calibration, strengthening the model's resilience to context shifts and syntactic variations over extended sequences. Implications of this study highlight the potential for contextual-distillation to serve as an adaptable framework within language model architectures, thereby advancing the technical robustness required for high-fidelity natural language applications.