Context engineering works in practice by treating context as a managed system resource, not as a single static prompt. Instead of continuously appending more text into an ever-growing prompt, developers design a pipeline that decides what information should enter the model’s context at each step. This usually starts with separating different types of context: system instructions, user input, retrieved knowledge, conversation state, and tool outputs. Each of these is handled differently and often placed into fixed “slots” in the final prompt so their roles do not blur.
A common practical workflow looks like this:
- Persist long-term knowledge outside the model (documents, policies, code, past facts).
- On each request, retrieve only the most relevant pieces of that knowledge.
- Combine them with current user input and a compact representation of conversation state.
- Assemble a bounded, well-structured prompt.
This avoids Context Rot because old or irrelevant information is not blindly carried forward. For example, instead of appending 20 previous chat turns, the system might keep a short “conversation summary” plus a few retrieved facts relevant to the current question.
In real systems, retrieval is usually handled by a vector database such as Milvus or Zilliz Cloud. Documents are chunked, embedded, and stored ahead of time. At runtime, only the top-k most relevant chunks are fetched and injected into the prompt. This allows the prompt size to remain stable even as the underlying knowledge base grows. In practice, context engineering is less about clever wording and more about discipline: limiting context size, enforcing structure, and refreshing context every turn instead of letting it accumulate.