Effective prompting is essential for maximizing the output from large language models, involving clear instructions and contextual guidance. Supervised fine tuning adapts pre-trained models to specific tasks using labeled datasets, enhancing performance for targeted user scenarios. Additionally, reinforcement learning with human feedback mimics a reward system, allowing models to learn from interactions and improve over time.
