How do you prevent context window overflow in LLM applications?

Context overflow is handled using summarization, truncation strategies, and retrieval-based memory systems.

LLMs have fixed context limits, so long conversations or documents must be managed carefully. Strategies include sliding window context, recursive summarization, retrieval-augmented memory injection, and importance-based pruning of tokens. These ensure relevant information is preserved while staying within token limits.