Teams need context engineering as soon as prompt length or interaction count becomes non-trivial. This often happens earlier than expected. A system that works well in a demo with a few short prompts may start failing once users ask follow-up questions, upload documents, or run longer sessions. That is usually the first sign that unmanaged context is becoming a problem.
Another clear signal is inconsistency. If users report that the system sometimes follows instructions and sometimes ignores them, or if answers drift over time in a single session, context engineering is needed. These are not model quality issues; they are context management issues. Teams often try to “fix” them by adding more text to the prompt, which usually makes things worse.
Teams building retrieval-augmented systems almost always need context engineering from day one. Storing knowledge externally and retrieving it dynamically is the only scalable approach. Vector databases such as Milvus and Zilliz Cloud enable this pattern and make it practical to manage context as a first-class system component. The earlier teams adopt context engineering, the easier it is to maintain quality as the application grows.