#training-tuning

LoRA, or low-rank adaptation, is a way to adapt a large pretrained model by training a small number of extra parameters instead of updating all of the model’s weights. Fine-tuning a large model the traditional way can be expensive in memory, storage, and training time. LoRA solves that by letting you specialize a model for a task while keeping the original model frozen. You’d reach for LoRA when you want: lower training cost than full fine-tuning multiple task-specific variants of the same base

Fine-tuning is the process of taking a pre-trained model and training it a little more on a smaller, task-specific dataset so it behaves better for your use case. Pre-trained models are general-purpose: they know a lot, but not your company’s style, domain vocabulary, or output format. Fine-tuning helps when you want the model to: follow a specific tone or schema consistently, handle niche domain language, improve performance on a narrow task, reduce prompt engineering overhead for repeated work

RLHF, or reinforcement learning from human feedback, is a way to train a model so its outputs better match what people prefer, not just what is statistically likely. A plain language model learns to predict text. That is useful, but it does not automatically make the model helpful, honest, safe, or aligned with user intent. RLHF is used when you want to steer a model toward human preferences: better answers, fewer toxic outputs, more helpful refusals, and responses that fit product goals. In