Definition
Context engineering expands prompt engineering into a broader workflow discipline. It asks what the model must know, retrieve, remember, and verify to complete a task reliably.
How it works
Teams define source data, system instructions, user context, examples, tool access, output schemas, and evaluation checks before asking the model to generate an answer.
Why it matters at work
As teams move from simple chat to AI workflows, the bottleneck becomes context quality. Better context produces better answers and safer automation.
Workplace example
A product team connects customer feedback, roadmap constraints, and scoring rules before asking AI to summarize feature priorities.
Frequently Asked Questions
How is context engineering different from prompt engineering?
Prompt engineering focuses on instructions. Context engineering includes instructions plus retrieval, memory, tools, examples, data quality, and evaluation design.