Adaptation & Alignment

Pretrained models are impressive generalists, but they're rarely ready for production use straight out of the box. The gap between "a model that can predict the next word really well" and "a model that helpfully follows your instructions and refuses harmful requests" is enormous, and closing it requires deliberate adaptation. This category covers the techniques used to take a powerful but raw pretrained model and shape it into something genuinely useful and safe. Fine-tuning adjusts the model's behaviour using additional training on specific data. Alignment techniques like Reinforcement Learning from Human Feedback (RLHF) teach the model to produce outputs that humans actually prefer, rather than just statistically likely text. Newer methods like Low Rank Adaption (LoRA) make adaptation more accessible and affordable by modifying only a small fraction of the model's parameters. These techniques sit at the heart of what makes modern AI assistants feel helpful rather than chaotic. They're also where many of the most important decisions get made about what an AI system will and won't do - decisions that directly affect every user. If you're considering customising an AI model, understanding these approaches helps you choose the right method for your needs and budget.