Context Engineering

Context Engineering is the art of designing the complete context in which an AI agent operates — a much broader concept than prompt engineering (which focuses on individual queries). Context engineering encompasses: the agent's config file (glossary of terms, project structure, conventions), skills (instructions for specific tasks), structured data (Schema.org, JSON, graphs), cross-session memory (persistence, memory), and contextual domain data (industry knowledge). Better context engineering creates smarter agents; they know concepts, understand goals, and know which tools to use.

In semantic audits, context engineering is foundational: The agent's config file defines that the agent knows concepts like EAV, CSI, Topical Authority; skills give it procedures; structured data gives it facts.

For example, an agent with good context engineering knows that 'probate' is a legal entity and automatically applies the EAV extraction tool. Without context engineering, it treats 'probate' as a random keyword.

In practice, invest in context engineering as much as in prompt engineering: a 50-line config file with context delivers better results than a perfect prompt without context.