If you’ve ever wanted to build your own “ChatGPT that talks like you” or answers questions about your company/domain, this video is my step-by-step walkthrough of fine-tuning LLMs locally. I break down what fine-tuning actually is (teaching a pre-trained model new patterns without retraining from scratch), why it matters, and where it fits alongside RAG and prompt engineering. I also compare full fine-tuning vs PEFT, because honestly PEFT (LoRA/QLoRA-style approaches) is the reason this is even practical for most of us without burning cloud money. Then I jump into a real demo: I fine-tune TinyLlama (1.1B) on my own laptop (32GB RAM, Ryzen 8-core CPU, no GPU). I show the exact flow: load the base model, wrap it with a LoRA/PEFT config, load and tokenize an instruction-response dataset, tune training parameters (learning rate + epochs are where I spent most of my time), run training, and save the adapter/model locally. Training took ~30 minutes on CPU, and it took me a few attempts to get clean outputs—because fine-tuning is 90% data prep and 10% code. Key takeaways: clean, well-formatted datasets decide your results; tuning params can make or break training; and for production-grade fine-tuning, better hardware + better evaluation frameworks matter. Fine-tuning gives you focus, memory, and style—but it complements RAG, it doesn’t replace it.