TL;DR
The design rationale of structuring and managing the diverse information ecosystems, tools, and databases that an AI model retrieves during runtime to make decisions.
Context engineering goes beyond traditional prompt writing by systematically building pipelines that feed documents, memory, environmental variables, and APIs into a model's contextual window. Since large language models output answers exclusively based on data visible at inference time, carefully formatting this input substrate directly dictates the quality of their reasoning. It manages the entire information flow that defines how an AI agent interacts with its surrounding enterprise ecosystem.
Why this matters for your business
Providing correct context is paramount to preventing hallucinations and ensuring corporate data is queried safely. Proper context engineering bridges the gap between general-purpose base models and highly customized business tools built to perform complex, domain-specific tasks.
