The MoE concept is a type of ensemble learning technique used in generative AI to enhance the capabilities of models, especially in handling complex tasks. It introduces the idea of dividing the model into specialized components, or "experts," and training the experts on specific subtasks of a complex predictive modelling problem. These experts work together, and their outputs are combined to produce the final prediction or generation.

Training a model within the ensemble exclusively on a subset of the data can lead to optimal performance, narrowing the model's focus. Mixture of Experts has been applied in various generative AI applications, including natural language processing, image generation, and speech synthesis. It has shown success in improving the model's performance and adaptability in these domains.