This thesis explores the use of custom loss functions to the finetuning of Small Language Models (SLMs) applied to recipe generation. With the exponential growth of deep learning, Large Language Models have proven themselves to have incredible text generation capabilities. Standard training frameworks, however, which employ Cross-Entropy loss, commonly disappoint in more demanding fields requiring high accuracy of facts and numbers, for instance the generation of procedural texts such as cooking recipes. This work tackles the intrinsic limitation of the standard solution that treats all the words indifferently, giving rise to the model’s inability to appropriate the important but frequently statistically scarce ingredients of a recipe.