How do you handle LLM token limits in a production Spring AI application?

Each model has a fixed context window; if history + RAG context + system prompt exceeds it, the API errors out. Mitigate with a memory advisor that trims the oldest messages as the limit approaches, periodic summarization of old messages, and capping how many chunks RAG retrieval injects.