No, context engineering is not the same as prompting, although prompting is one small part of it. Prompting focuses on how you phrase instructions or questions in text. Context engineering focuses on the entire lifecycle of information that enters the model: system instructions, retrieved knowledge, conversation history, summaries, tool outputs, and constraints. Prompting is about how you ask; context engineering is about what the model sees and when.
A well-written prompt cannot compensate for poorly managed context. For example, you can write a perfect instruction saying “use only the retrieved documents,” but if the prompt also contains outdated documents or irrelevant conversation history, the model may still produce incorrect answers. Context engineering addresses this by filtering, ranking, summarizing, and refreshing context instead of letting it accumulate unchecked.
In modern systems, context engineering often relies on external memory. Instead of embedding everything in a static prompt, teams store knowledge in a vector database such as Milvus or Zilliz Cloud and retrieve only what is relevant for each request. Prompting still matters, but it becomes one component inside a broader context management system rather than the entire solution.