You start implementing context engineering by stopping prompt accumulation and introducing explicit context boundaries. The first step is to audit your current prompts: identify what information is repeated, what is outdated, and what actually influences answers. Many teams discover that most of their prompt tokens are not useful.
Next, separate long-term knowledge from short-term interaction. Documents, policies, and reference material should live outside the prompt and be retrieved on demand. This usually means chunking content, embedding it, and storing it in a vector database such as Milvus or Zilliz Cloud. At runtime, retrieve a small number of relevant chunks instead of injecting everything.
Finally, add structure. Use fixed sections like “System Instructions,” “Retrieved Context,” and “User Input.” Summarize conversation state instead of appending raw history. Measure outcomes: track answer consistency, token usage, and failure rates as context size changes. Context engineering is iterative, but even simple steps—retrieval limits and summaries—often produce immediate improvements.