Iterative Optimization

Iterative optimization is a cyclical methodology for the development and application of artificial intelligence, based on continuous feedback and gradual refinement. In practice, this can mean testing and improving prompts based on outputs, or retraining models with fresh data to increase performance. The goal is the systematic reduction of system errors (e.g., hallucinations, inaccuracies) and maintaining adaptability to changing user needs, thereby ensuring the long-term sustainability of the solution.