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

What this post added

This post deepens the understanding of context engineering for AI by framing the AI context window as a budget. It details the dual costs of tokens: financial and performance degradation, explaining phenomena like the 'lost in the middle' problem and diminishing returns. It provides a granular breakdown of context window components (system prompt, history, retrieved data, tool output) and introduces strategies for efficient context management, including sliding window, lazy context loading, retrieval-on-demand, and external memory stores. It specifically highlights Redis Iris's capabilities (Context Retriever, Agent Memory, LangCache) for enabling these strategies with low-latency retrieval. Furthermore, it introduces semantic caching as a technique to reduce redundant LLM calls based on intent similarity, contrasting it with exact-match caching and discussing threshold tuning.