Top five essential context window concepts in large language models

This blog outlines five essential concepts that explain how large language models process input within a context window. Using clear examples and practical insights, it covers foundational ideas like tokenization, sequence length and attention. The goal is to help readers better understand how context affects model behavior in AI applications. We also present results from an analytical model used to estimate system behavior, to show how scaling input and output sequence lengths impacts response time. The results highlight how decoding longer outputs takes significantly more time, pointing to the importance of fast memory systems like HBM in supporting efficient inference at scale. These concepts are useful for anyone working with or designing prompts for generative AI systems.