LLMs handle context switching in conversations by analyzing the input and determining the most relevant topic based on the provided text. If a user changes topics abruptly, the LLM attempts to adapt by focusing on the new input while retaining the context within the conversation's scope. For example, if a user asks about the weather and then shifts to asking about travel plans, the model responds to the latest query while maintaining coherence.
However, LLMs are limited by their input window size, which constrains how much past context they can recall. This means they may lose track of earlier parts of the conversation if the input exceeds their maximum context length. Sudden, unprompted shifts in topics can also confuse the model, leading to less coherent responses.
To improve context handling, developers can design systems that provide structured prompts or include key information from prior exchanges. Combining LLMs with external memory mechanisms or conversation history tracking can enhance their ability to manage context switching effectively.