Generative AI works through the use of neural networks, specifically Recurrent Neural Networks (RNNs) and more recently, Transformers. Here’s a simplified breakdown of how it functions:
Data Collection: To begin, a substantial amount of data related to the specific task is gathered. For instance, if you want to generate text, the model needs a massive text corpus to learn from.
Training: The neural network is then trained on this data. During training, the model learns the underlying patterns, structures, and relationships within the data. It learns to predict the next word, character, or element in a sequence.
Generation: Once trained, the model can generate content by taking a seed input and predicting the subsequent elements. For instance, if you give it the start of a sentence, it can complete the sentence in a coherent and contextually relevant manner.
Fine-Tuning: Generative AI models can be further fine-tuned for specific tasks or domains to improve the quality of generated content.
