To effectively manage long AI conversations beyond simple truncation, a key strategy is to periodically summarize older parts of the dialogue, retaining essential information while significantly reducing token count. Additionally, implementing a dynamic context window allows you to prioritize and include only the most relevant recent messages along with a concise summary of the older conversation. Another powerful approach involves using an external memory system, like a vector database, to store the entire history and then retrieve only the most pertinent snippets to provide targeted context for the AI, optimizing both cost and performance.