Effectively training language models starts with high-quality, structured data. For a model to truly learn and apply knowledge, it needs consistent, reliable data that forms the foundation of its understanding. Quality data enables the model to build connections and recognize how different pieces of information fit together. Beyond raw data, providing the model with context is essential. One effective method is to use stories that frame the information in a way that makes it relatable and meaningful.

Creating good stories involves adding a wealth of related details while staying grounded in structured data. These stories help the model understand not just isolated facts but the broader context they belong to. For example, in the field of healthcare, structured data about treatments can be used to craft narratives about how specific symptoms led to particular diagnoses and treatments. These stories provide the model with insights into the interconnected nature of medical knowledge, teaching it how symptoms, processes, and outcomes relate to each other.

Such contextualized stories play an important role in helping the model learn how knowledge fits into larger systems and how it can be used. Instead of limiting itself to memorizing terms or concepts in isolation, the model gains a deeper understanding of how information interacts in real-world applications. This makes it better at adapting its responses to practical scenarios, like answering complex questions or solving problems where context is key.

When models are trained using detailed stories from structured data, particularly in specialized areas like healthcare, their ability to apply knowledge improves significantly. These stories not only enhance learning—they also prepare the model to make informed, context-aware decisions that are closer to how humans approach complex issues. Crafting narratives from structured data is a powerful way to unlock the full potential of language models and bring out their utility in a meaningful and impactful way.