Building a generative AI solution involves several key steps. First, define the problem you're aiming to solve and gather a large dataset that will train the AI model. Ensure the data is diverse and high-quality. Next, select the right machine learning framework, such as TensorFlow or PyTorch, and choose a model architecture like GPT or GANs based on your needs. After that, train the model using powerful GPUs or cloud services, fine-tuning it as you go. Once trained, evaluate the model's performance, iterating as necessary. Finally, deploy the solution, integrating it with your applications or systems for real-world use.