I’m working on a new guide titled “5 tips for getting accurate results when coding with AI.” We’re looking for experienced software engineers and developers to share their tactics for making AI tools more precise, reliable, and production-ready. We want personal insights and workflows. Please include your LinkedIn profile for verification and a professional email address. We will contact you directly to verify your submission. Questions: - Should engineers shift from “prompting” to “context engineering”? - How should developers use reasoning traces to catch errors before code is generated? - Is test-driven generation a reliable way to prevent logical errors? - What is your most effective verification loop for ensuring AI-generated code is safe for production? - Is model-swapping a viable accuracy strategy? - What is the ideal task size before AI performance declines?