Context engineering for AI: what it is & how to build it

Context engineering is a crucial discipline in AI app development, focusing on determining what data is fed into an AI model's context window during inference to enhance reliability and performance. Unlike prompt engineering, which deals with crafting a single instruction, context engineering involves a systematic approach to managing all inputs, such as system instructions, conversation history, and tool outputs, that influence the model's reasoning. This practice is essential for multi-step agentic systems where context window management can prevent issues like context overflow and degradation. Effective context engineering involves four key operations: writing context to external storage, selecting relevant context for each step, compressing information to preserve signal, and isolating tasks to prevent context pollution. The infrastructure supporting these operations requires robust systems for retrieval, memory management, semantic caching, and real-time data handling, with solutions like Redis offering an integrated platform to manage these complexities efficiently.