Book: Prompt Engineering Pocket Guide: Techniques for Getting the Most from LLMs Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools Me: xgabriel.com | GitHub You have seen the screenshot. A LinkedIn post, two thousand likes, the headline reading something like "Google found the magic phrase that makes ChatGPT smarter." The phrase is Take a deep breath and work on this problem step-by-step. Someone on your team pastes it into the system prompt the next morning. It sits there for a year. Nobody re-tests it. Two model upgrades later, you have a line in production that nobody can defend and nobody wants to be the person who deletes. This is what every prompt-engineering "trick" turns into if you do not own an eval harness. The trick is real — until it isn't. The model under it changes. The post-training changes. The trick's effect drifts.…