Context engineering is important because LLMs do not manage context intelligently on their own. Models process whatever tokens they are given, but they do not understand which information is most important unless the system makes that clear. As prompts grow longer, attention becomes diluted, and critical instructions or facts can lose influence. Without context engineering, even powerful models can behave inconsistently or produce low-quality results.
In real applications, this directly affects reliability. For example, a support chatbot may start by correctly identifying a customer’s plan or environment, but after several turns, it may give advice meant for a different plan or configuration. This is not because the model “forgot,” but because the relevant context was buried among many other tokens. Context engineering mitigates this by explicitly controlling what stays in context and what is summarized, removed, or reloaded on demand.
Context engineering is also essential for scaling. As applications grow to include large document sets, codebases, or multi-step workflows, it becomes impractical to include everything in the prompt. Retrieval-based designs store knowledge externally and fetch it when needed. Vector databases like Milvus and Zilliz Cloud make this possible by enabling fast semantic retrieval. This allows systems to remain accurate and efficient as they scale, rather than becoming brittle due to oversized prompts.