Context engineering uses vector databases as external, queryable memory. Instead of embedding all knowledge directly into prompts, applications store embeddings in a vector database and retrieve them dynamically. This allows the system to decide what context to include at runtime rather than at design time.
The workflow is straightforward. First, documents are chunked into manageable pieces and embedded using an embedding model. These embeddings are stored in a vector database along with metadata such as source, topic, or timestamp. At query time, the user’s input is embedded and used to search the database for the most similar chunks. Only the top-ranked results are injected into the prompt.
Vector databases like Milvus and Zilliz Cloud support filtering, ranking, and efficient similarity search, which are essential for context engineering. They allow developers to constrain context by relevance, recency, or domain, ensuring that the model sees focused, high-quality information. This controlled retrieval is what makes context engineering reliable and maintainable in production systems.