Context Engineering

Context engineering is the natural evolution of prompt engineering. Instead of just "how do I word this prompt," the question is: what's the full set of inputs the model needs — system prompt, retrieved documents, tool definitions, conversation history, examples, user query — and how should they be assembled?

In 2026, context engineering has become the dominant skill for building LLM-powered products. Modern frontier models are forgiving of mediocre prompting; they're brutal about missing context. Most failure modes in production AI products trace back to context the model didn't have, not to instructions it didn't understand.

For operators evaluating AI products, ask: "what context does the agent have access to, and how does it decide what to load?" Vendors who can answer well are usually shipping more reliable products. Vendors who pretend it's all about a clever prompt are usually behind.