This episode provides an overview of finetuning, a method for adapting AI models to specific tasks by adjusting their internal parameters, contrasting it with prompt-based techniques which rely on instructions. It explains that finetuning often improves task-specific abilities and output formatting, although it requires greater computational resources and machine learning expertise compared to prompting. The text explores memory bottlenecks in finetuning large models, highlighting techniques like quantization (reducing numerical precision) and Parameter-Efficient Finetuning (PEFT), with a focus on LoRA (Low-Rank Adaptation) as a dominant PEFT method. Finally, the source discusses the strategic decision of when to finetune versus use Retrieval Augmented Generation (RAG), suggesting a workflow for choosing between adaptation methods, and introduces model merging as a complementary approach for combining models.