The output generation process in artificial intelligence models is based on a set of decoding strategies that determine the quality and accuracy of the final text. This process begins with the analysis of "logits" returned by the model for each position in the input sequence.
This section examines pivotal techniques such as Greedy Decoding, Temperature Sampling, Top-k methods, and Nucleus Sampling. Additionally, more advanced concepts such as Repetition Penalties, Beam Search, and the application of stopping conditions and structured constraints for more precise control over the model's output are explored.