Fine-tuning & Model Basics

by bam

You can prompt, retrieve, and orchestrate. Now learn when to change the model itself. This builder course demystifies how models learn (pretraining → post-training and RLHF), what fine-tuning really is, and the decision that matters most: when fine-tuning beats prompting or RAG, and when it's an expensive mistake. We cover the method families (full fine-tuning, LoRA/PEFT, instruction and preference tuning), how to build a real training dataset where quality beats quantity, the training loop and how to avoid overfitting, how to evaluate a fine-tuned model honestly, the cost/latency/hosting tradeoffs of API fine-tuning vs open weights, open vs closed models and their licenses, and the real risks: catastrophic forgetting, data leakage, and bias amplification. You leave with a 'should I fine-tune?' framework and the trust DNA to use it: evaluate honestly. Fine-tuning isn't magic. B1 (Prompt Engineering) is the recommended prerequisite.