Context engineering should be used by anyone building LLM applications beyond simple, single-turn prompts. If your application involves multi-turn conversations, document-based question answering, code assistance, agents, or workflows that span multiple steps, context engineering quickly becomes necessary. Without it, behavior degrades as complexity increases.
Product teams building chatbots, internal tools, and AI assistants are prime candidates. These systems often need to respect constraints, remember user preferences, and reference large bodies of knowledge. Context engineering ensures that the assistant stays grounded and consistent across interactions. It is especially important in enterprise settings, where incorrect or inconsistent responses can have real operational consequences.
Even small teams and individual developers benefit from context engineering once their application grows beyond a prototype. If you are retrieving data from documentation, tickets, or knowledge bases, using a vector database such as Milvus or Zilliz Cloud allows you to apply context engineering principles early. This prevents painful rewrites later when prompt-only approaches stop working.