Writing

Your AI is already writing your code. The real question: can anyone explain why that code exists six months from now? I designed and ran a Spec-Driven Development operating model at a US-based automotive major — the team writes specs, AI agents build the code, and nothing merges without clearing human checkpoints and automated gates. ~3,000 lines of human-authored specification governing ~28,600 lines of production code — roughly 10× leverage. 300 acceptance criteria traced from intent to test. 0 architecture violations.

Text2SQL accuracy is not primarily a model problem — it's a context problem. Database schemas alone leave LLMs guessing at business meanings, leading to hallucinated columns and incorrect joins. By layering semantic enrichment — concepts, rules, aliases, and join patterns — on top of raw schema, you transform your chatbot from unreliable to production-ready. The knowledge graph becomes queryable context, not static documentation.