This podcast offers a comprehensive overview of fine-tuning large language models (LLMs), exploring both foundational principles and advanced techniques. It details a seven-stage pipeline for fine-tuning, covering everything from initial data preparation and model initialization to training setup, evaluation, deployment, and ongoing monitoring and maintenance. The text also discusses various parameter-efficient fine-tuning (PEFT) methods and contrasts approaches like Retrieval-Augmented Generation (RAG) with fine-tuning for different use cases. Furthermore, it addresses the integration of LLMs with multimodal data, including vision and audio, and highlights key open challenges and research directions in the field.