2. Understanding content generation

Once a model has been trained, it can be used to generate content. This process is called inference. The model uses the knowledge acquired during training to create new data, be it text, images, music or video. Let's take the example of a language model like GPT and try to break down the content generation process.

Initially, the user provides input in the form of a prompt: this prompt may be a question or an instruction. This text is then encoded, i.e. divided by the model into units (tokens) corresponding to words or groups of words. The tokens are then transformed into mathematical vectors (numerical representations) using the transformer technology mentioned above.

The answer is generated in the form of decoding using various probabilistic methods. The model generates the answer word by word (or token by token), iteratively, predicting...