Context windows in AI: why every token is a budget decision

Large language models (LLMs) now have the capability to support extensive context windows, but using them to their full capacity can be costly and may degrade reasoning quality. A context window is a fixed-size limit for tokens that an LLM can process in a single inference pass, encompassing both input and model-generated output. As context size increases, the cost of processing each token rises, while reasoning quality can diminish due to factors like the volume and position of input, leading to "lost in the middle" issues. Effective context management involves strategically selecting what information enters the context window, keeping unnecessary data in fast external storage until needed, and employing techniques like semantic caching to reduce redundant processing. Redis Iris provides tools such as Context Retriever and LangCache, which facilitate efficient context management and retrieval, ensuring that LLMs use only relevant data for each interaction, thus maintaining performance and cost-effectiveness.