Context Engineering

The practice of selecting, structuring, and dynamically managing information provided to AI systems to optimise response quality, reliability, and relevance beyond basic retrieval-augmented generation approaches. Context engineering involves strategic decisions about what background information to include, how to organise it, when to update it, and how to handle context limitations across extended interactions. Businesses can leverage context engineering to build more reliable AI systems that maintain accuracy whilst managing computational costs and token limitations. Applications include designing effective knowledge bases for customer support AI, structuring document repositories for analysis tasks, and creating adaptive context strategies for long-running AI conversations. **See also:** AI Integration service