Context engineering and RAG are tightly linked: RAG retrieves documents or memory based on embeddings, and context engineering controls how those retrieved pieces are integrated into the prompt. In a typical pipeline, you embed the user query, use a vector database to fetch top-k semantically similar context, then format and inject those into your prompt with instruction, memory, and retrieved text.
But retrieval alone is insufficient—you need context engineering to filter, rank, compress, and guard the retrieved pieces. Otherwise, injecting raw documents may overflow token windows or introduce conflicting facts. Good context engineering ensures the retrieved context is consistent, high-quality, and properly placed in the prompt. Vector DBs supply the raw candidates; context engineering refines and slots them into a structured input for the model.
Thus, vector DBs like Milvus and Zilliz Cloud act as memory or context stores, and context engineering is the orchestration layer: deciding when to retrieve, which embeddings to query, how many results, how to format them, and when to fall back. Together, they form the backbone of systems that go beyond static prompts into dynamic, grounded AI.