From chaos to context: Building an AI dev workflow

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There's a particular kind of frustration that happens when prompting an AI assistant with the same correction multiple times in a single session. The marvels of modern large language models (LLMs) make it so you're working with the most enthusiastic apprentice you'll ever have. However, that apprentice also happens to be an amnesiac. “Yes, I really do want my commit messages formatted that way, we've had this conversation three times already.”Or perhaps you've experienced the trouble of trying to orchestrate several parallel AI sessions, only to watch them independently start solving the same problems and deleting each other's work.These frustrations led me down a path of iteration. I worked on early renditions of two GitLab AI features: Explain this vulnerability and Resolve this vulnerability features. They felt naive at the time, and I wanted more from them.Agentic AI delivered that. Instead of one-shot suggestions I had to prompt for and paste back, an agent could read the codebase, make the change, and run the tests on its own. It was doing the work rather than just advising on it. From there, I moved through GitLab Duo Custom Agents, VS Code integrations, and eventually OpenCode, an open source agent that describes itself as helping you "write code in your terminal, IDE, or desktop."Along the way, I've distilled what's been working for me. AI coding assistants are genuinely transformative, but they need your engineering instincts to guide them. They amplify both good decisions and bad ones, so direction matters. The tools will keep changing, but here's what's helped …

Vollständiger Originalartikel ↗

• Betroffenheit im eigenen Stack prüfen: Versionen/Komponenten abgleichen.
• Originalquelle / Hersteller-Advisory lesen: https://about.gitlab.com/blog/building-an-ai-dev-workflow/
• Verfügbaren Patch oder Workaround einspielen und dokumentieren.