Context engineering is an advanced approach to working with large language models that goes beyond simple prompt crafting. It involves strategically managing the entire context window with curated information including task descriptions, examples, retrieved documents, conversation history, and external data. Unlike prompt engineering which focuses on clever single-line instructions, context engineering manages knowledge flow, memory systems, and information retrieval to build production-grade AI applications. The approach addresses context window limitations through techniques like chunking, filtering, and dynamic knowledge injection, making it essential for enterprise AI systems and autonomous agents that require consistent, accurate outputs.
Table of contents
Context WindowFailing Long Context ExampleWell-Engineered Shorter Context Example1. Knowledge (Domain or Reference Data)2. Instructions (Control and Behavior Shaping)Tools and API Calls (Dynamic Context Injection)Filtering and PrioritizationCommon Strategies for Context EngineeringSort: