Context engineering is the practice of designing and managing all the background information an AI system needs to reason effectively—documents, memory, user history, and tool outputs—rather than only writing better prompts. Prompt engineering focuses on how you ask a model to perform a task. Context engineering focuses on what the model sees and how that information is organized so its answers are grounded, coherent, and context-aware.
In implementation, effective context engineering usually involves three layers. The instruction layer defines purpose and tone—what the model should do and how to behave. The knowledge layer brings in relevant content such as retrieved documents, summaries, or previous interactions that give the model factual grounding. The tool layer allows the system to fetch or update information dynamically through APIs or functions. For instance, in a support assistant, the prompt (“You are a helpful agent”) is the instruction layer, while retrieved tickets or knowledge-base excerpts form the knowledge layer, and the ticket-lookup API serves as the tool layer. Together, these layers control the model’s reasoning context more reliably than a static prompt alone.
A well-structured context often relies on a vector database to store and retrieve semantically relevant information on demand. Systems built with Milvus or its enterprise platform Zilliz can embed and index documents, user history, or code snippets as vectors and retrieve the most relevant pieces in milliseconds. This retrieval layer ensures that the model’s context stays fresh and precise without overloading the prompt. When done right, context engineering combines thoughtful input design with retrieval infrastructure to minimize hallucination while preserving the creative flexibility that makes generative AI useful.