Developing a Generative Artificial Intelligence model is resource intensive. Companies looking to leverage Generative AI have the option to either use it out-of-the-box or fine-tune them to perform specific tasks. How can enterprises efficiently fine-tune their language models to become less biased and better at solving specific tasks? Collect and fine-tune an existing foundational model with a relatively small sample of demonstration data for specific tasks (supervised) Use reinforcement learning and reward system to improve model outputs (RL) Enable human feedback such as rating or ranking the model outputs (HF) Train a language model from scratch unanswered