How AI Models Are Trained: Fine-Tuning, RLHF, LoRA and Quantization
Training a large language model is only the beginning of the story. After a model learns language from billions of examples during pretraining, engineers still need to make it more useful, safer, cheaper to run, and specialized for real-world applications. This is where a handful of complementary techniques come in: Fine-Tuning, RLHF, LoRA, and Quantization. Each solves a different part of the problem — specialization, alignment, training cost, and deployment cost. Let’s walk through each one. ...