🔗 My LLM coding workflow going into 2026

AI coding assistants became game-changers this year, but harnessing them effectively takes skill and structure. These tools dramatically increased what LLMs can do for real-world coding, and many developers (myself included) embraced them.

At Anthropic, for example, engineers adopted Claude Code so heavily that today ~90% of the code for Claude Code is written by Claude Code itself. Yet, using LLMs for programming is not a push-button magic experience - it’s “difficult and unintuitive” and getting great results requires learning new patterns. Critical thinking remains key. Over a year of projects, I’ve converged on a workflow similar to what many experienced devs are discovering: treat the LLM as a powerful pair programmer that requires clear direction, context and oversight rather than autonomous judgment.

In this article, I’ll share how I plan, code, and collaborate with AI going into 2026, distilling tips and best practices from my experience and the community’s collective learning. It’s a more disciplined “AI-assisted engineering” approach - leveraging AI aggressively while staying proudly accountable for the software produced.

If you’re interested in more on my workflow, see “The AI-Native Software Engineer”, otherwise let’s dive straight into some of the lessons I learned.