What is Context Engineering?
The practice of deliberately designing what context an AI model receives: system prompts, retrieved documents, conversation history, tool outputs, and structured metadata. Context engineering treats the model's input as a first-class engineering artifact, not an afterthought. The quality of AI output is bounded by the quality of the context you provide.
My perspective
In practice
The difference between a mediocre AI workflow and a great one is almost always context, not model choice. Context engineering includes: curating knowledge bases for RAG, writing system prompts that encode domain expertise, structuring conversation history to prevent context rot, and designing tool schemas that give agents the right information at the right time. Teams that invest in context engineering see their AI systems get consistently better over time.