Context engineering scales to large datasets by decoupling model context from dataset size. Instead of attempting to place more data into the prompt as datasets grow, context engineering treats the prompt as a limited working memory and moves large-scale knowledge into external storage. This allows systems to scale from thousands to millions of documents without increasing prompt size or degrading model performance.
In practice, this is achieved by chunking large datasets into small, semantically meaningful units and indexing them for retrieval. When a user query arrives, the system retrieves only a small subset of relevant chunks rather than the entire dataset. This keeps the prompt size stable and predictable, even as the underlying corpus grows. For example, a documentation assistant may index hundreds of thousands of pages but only inject five short sections into the prompt for any given question.
Vector databases are central to this approach. By storing embeddings in a vector database such as Milvus or Zilliz Cloud, systems can perform fast semantic search across large datasets and return only the most relevant context. This makes context engineering fundamentally scalable: dataset growth affects storage and indexing, not prompt complexity. As a result, performance and answer quality remain stable as systems scale.