Context-Aware AI in Analytics: Useful Answers vs Confident Guesses

This post details the evolution of Hex's AI capabilities towards context-aware AI in analytics. It explains how combining LLMs with specific data, definitions, and business logic (schema, metric definitions, permissions, workspace history) leads to more accurate and trustworthy answers compared to generic chatbots. It highlights the application of RAG principles with richer, structured context (live schema metadata, governed metric definitions, query history, team-specific rules) and contrasts 'bolt-on' AI with AI native to the analytical workflow. The post emphasizes the importance of governed semantic layers and introduces Context Studio for observability into context breakdown and quality issues. It also discusses trade-offs like context accuracy, freshness, predictability, and user control, advocating for visible AI reasoning and the ability to inspect/override decisions.