LLMs rely on their context to generate the next set of tokens. Over a long trajectory, the context fills with numerous types of components: a system prompt, past conversation turns, instructions that act on retrieved documents, tool responses or user data, requirements constraining the allowed output, and reasoning chains. The context carries a rich structure, most of which is either implicit to the model or encoded in formats such as JSON that were not designed to be digested by LLMs. This is one of the main reasons LLMs, particularly those on the smaller end of the scale, are brittle and sometimes fail in unexpected ways. The lack of explicit structure can also be exploited by attackers, for example, in the form of prompt injections.