In this episode, we delve into a newly published white paper that outlines a cutting-edge pipeline for enhancing language models through knowledge distillation and post-hoc mechanistic interpretability analysis. We explore how the approach integrates data enrichment, teacher pair generation, parameter-efficient fine-tuning, and a self-study loop to specialize a base language model—particularly for cybersecurity tasks—while preserving its broader language capabilities. We also discuss the newly introduced Mechanistic Interpretability Framework, which sheds light on the internal workings of the distilled model, offering insights into layer activations and causal pathways. Whether you're building domain-specific AI or curious about making large language models more transparent, this conversation reveals how domain expertise and interpretability can come together to create more trustworthy and efficient AI systems.