Designing computation workflows for AI applications faces complexities, requiring the management of various parameters such as prompts and machine learning hyperparameters. Improvements made post-deployment are often manual, making the technology harder to update. Traditional optimization methods like Bayesian Optimization and Reinforcement Learning often call for greater efficiency due to the intricate nature of these systems.…
