Large Language Models (LLMs) and AI agents are only as effective as the context they receive. A well-crafted prompt with rich, relevant background information can yield dramatically different results than a bare-bones query. Recent studies show that LLM performance can vary by up to 40% based solely on the quality and relevance of input context, making the difference between a helpful AI assistant and a confused chatbot.

This reality has given rise to a new discipline: Context Engineering is to AI what Prompt Engineering was to GPT-3. While prompt engineering focused on crafting better individual requests, context engineering takes a systems-level approach to how AI applications understand and respond to their environment.