Context Engineering

Context engineering ensures AI models receive the right information at the right time. Unlike prompt engineering, which focuses on how you ask questions, context engineering focuses on what information the model has access to. This includes system prompts that set behavior and tone, RAG pipelines that inject relevant documents, conversation memory that maintains continuity, and few-shot examples that demonstrate desired outputs. When to use context engineering: Build chatbots or agents that need domain knowledge, create assistants that remember past interactions, or improve accuracy by grounding responses in retrieved documents. Common mistakes: Overloading context with irrelevant data (wastes tokens and can confuse the model), forgetting to refresh stale context, or providing conflicting information across different context sources.