This episode dives into fine-tuning large language models (LLMs), exploring key techniques like supervised, unsupervised, and instruction tuning. We highlight the critical role of high-quality data and parameter-efficient methods such as LoRA and QLoRA. Ethical considerations take center stage, with insights on bias mitigation, privacy, security, and the importance of transparency and governance in AI development. Finally, we discuss deployment strategies—cloud vs. edge computing—and the necessity of ongoing model maintenance and continuous learning.