Context Engineering vs Prompt Engineering: What Data Teams Need

This post introduces the concept of 'context engineering' as a necessary evolution beyond 'prompt engineering' for data teams using AI. It details the components of context engineering: endorsed tables, warehouse schema and table descriptions, workspace rules, semantic models, conversation history, and observability signals. It also outlines a practical, phased approach for data teams to implement these context layers, starting with endorsing tables and adding descriptions, then moving to workspace rules and semantic models. The post argues that context engineering is essential for ensuring trusted, consistent, and auditable analytical answers from AI, especially at enterprise scale, and contrasts this with the limitations of prompt engineering for complex data tasks. It also discusses how this approach addresses the problem of 'shadow AI' by making the governed path faster and more reliable.