Context Engineering

What Is Context Engineering?

While prompt engineering focuses on the individual question, context engineering goes a step further and shapes the entire information environment of an AI. This is especially relevant for businesses using AI agents, RAG systems, or chatbots. The quality of results depends less on the model than on what context you provide.

Context engineering goes far beyond classic prompt engineering. While prompt engineering focuses on formulating individual instructions, context engineering encompasses the entire information environment of an LLM: which documents are passed? In what order? Which system prompts set the frame? What tools are available? All of these factors determine the quality of AI responses at least as much as the actual question.

In practice, context engineering means systematically controlling what an LLM “knows” and “sees” before it responds. This includes selecting relevant documents via RAG systems, structuring system prompts, providing examples, and defining available tools. Agentic Memory is a domain where context engineering plays a central role: what an agent remembers and how it accesses that information determines its performance.

For businesses, context engineering is a key competency when deploying AI systems. A poorly designed context leads to inaccurate or irrelevant answers — regardless of how capable the underlying model is. Closely related is Haystack Engineering, which specifically deals with designing long context windows. Together, both disciplines form the foundation for professional AI applications.