𝟯𝟴% 𝗯𝗲𝘁𝘁𝗲𝗿 𝗔𝗜 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆. 𝗡𝗼 𝗻𝗲𝘄 𝗺𝗼𝗱𝗲𝗹. 𝗡𝗼 𝗻𝗲𝘄 𝗱𝗮𝘁𝗮. 𝗝𝘂𝘀𝘁 𝗯𝗲𝘁𝘁𝗲𝗿 𝗰𝗼𝗻𝘁𝗲𝘅𝘁. That’s the headline from a controlled NL-to-SQL experiment discussed by Manoj Shanmugasundaram in Metadata Weekly. Across 522 query evaluations, the only variable that changed was context quality — and it made all the difference. Concise, high-signal context (business definitions, SQL patterns, domain rules) drove a 38% accuracy gain. Verbose, catalog-style documentation? Performance dropped and costs rose. More words diluted the signal. The biggest lift wasn't on simple or extreme queries. It was on medium-complexity ones — the joins and aggregations that make up everyday analytics work — where focused context delivered a 𝟮.𝟭𝟱𝘅 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁. The mindset shift: metadata was built for humans to browse. Now it also needs to work for machines to reason. Most teams are still optimizing for readability, not machine usability. If your "talk to data" initiative is stalling, it might not be a model problem. It might be a context problem. Manoj breaks down what machine-usable context actually looks like — and how to get started without rebuilding your stack. Read it in Metadata Weekly: https://metadataweekly.substack.com/p/the-context-problem-nobodys-fixing