Context Window

The shift to 1 million token context windows in models like Claude 4.6 changes AI architecture. Learn about the 'lost in the middle' problem, latency issues, and when to choose RAG over context stuffing.

Explore how to overcome context window limitations using recursive summarization and map-reduce patterns for processing millions of tokens with LLMs.

Explore the breakthrough of Recursive Language Models (RLM) that solve context rot in GPT-5, enabling 10M+ token processing with lower costs and higher accuracy.