Low-Rank Adaptation (LoRA) Explained: Fine-Tuning LLMs Without Retraining Everything

Low-Rank Adaptation (LoRA) is one of the most powerful techniques for efficiently fine-tuning large language models. Instead of retraining billions of parameters, LoRA inserts small low-rank matrices into a Transformer’s layers, allowing models to adapt to new tasks with a fraction of the compute. In this video, we explain how LoRA works in LLMs, why it dramatically reduces training costs, and how it enables parameter-efficient fine-tuning. If you're learning about large language models, Transformer architecture, or AI systems engineering, understanding LoRA is essential for building efficie…