Context Engineering Best Practices: A Hands-On Guide

This post introduces the concept of 'context engineering' for AI analytics, defining it as the practice of building structured environments to make AI answers accurate and reproducible. It outlines four layers of context: endorsed tables and warehouse descriptions, workspace rules, semantic models, and observability/feedback loops. The post details how each layer addresses specific failure modes of AI agents, such as metric inconsistency, join proliferation, and confidently wrong answers. It emphasizes starting with endorsed tables and descriptions as the most immediate impactful step, and then progressively investing in workspace rules and semantic models based on observability data from Hex's Context Studio.