What is Context Window Optimization?
TL;DR
Techniques for efficiently utilizing an LLM's context window to maximize output quality.
Context Window Optimization: Definition & Explanation
Context Window Optimization encompasses techniques and strategies for efficiently utilizing the finite context window (maximum input token count) of LLMs to maximize output quality. While window sizes vary by model — GPT-4 Turbo at 128K, Claude 3.5 at 200K, and Gemini 1.5 at 1M-2M tokens — larger windows also mean higher costs, making optimization essential. Specific techniques include conversation history summarization and compression, selective injection of only relevant information via RAG, document chunking with priority ranking, prompt cache utilization (offered by Anthropic and OpenAI), and filtering out unnecessary information. To address the 'Lost in the Middle' problem (where information in the middle of long contexts tends to be overlooked), the practice of placing important information at the beginning or end of the context has also been adopted.