Context Engineering 101: How ChatGPT Stays on Track

Context engineering manages what information an AI model sees when generating responses. Unlike prompt engineering which focuses on phrasing, context engineering determines which data enters the limited context window at each step. Key components include system prompts, message history, examples, tools, and external data. For complex tasks, strategies include retrieval augmented generation (RAG), just-in-time retrieval, progressive disclosure, context compaction, structured note-taking, and sub-agent architectures. The goal is providing relevant information without overwhelming the model, especially important for multi-step agent workflows where context rot can bury critical details.