Knowledge Distillation (KD) is an effective technique for compressing large language models through the teacher-student framework. Previous work in feature distillation mainly applied an exact matching between the hidden representations of the student and the teacher. However, as the student has a lower capacity compared to the teacher, it may struggle to mimic its exact hidden representations. This leads to a large discrepancy between their features as shown in preceding research. Therefore, we propose intra-class similarity-guided feature distillation, a novel approach to make the task easier for the student. In this work, we map each sample representation by the student to its K nearest neighbor samples representations by the teacher that are within the same class. This method is novel and can be combined with other distillation techniques. Empirical results show the effectiveness of our proposed approach by maintaining good performance on benchmark datasets.