How I’d Learn AI Engineering in 2026

Louis Bouchard, an AI educator, released a rebuilt public roadmap on GitHub called Start AI Engineering, designed for learning AI engineering in 2026. The roadmap includes free videos, books, documentation, courses, projects, and job resources, emphasizing that with AI agents like Codex, Claude Code, and Cursor able to generate apps quickly, engineers must focus on architectural decisions and edge cases rather than just generating code.

Member-only story Featured How I’d Learn AI Engineering in 2026 You can open Codex, Claude Code, or Cursor today, describe an app in English, and get a convincing result in minutes. The agent can scaffold a retrieval-augmented generation RAG pipeline, add API routes, write tests, and prepare a deployment. That speed is genuinely useful. It is also one of the easiest ways to fool yourself while learning AI. The app appearing on screen does not prove that you understand why its architecture makes sense, what happens outside the happy path, or whether its output is good. When the first version is easy to generate, your value moves to the decisions around it. That is the idea behind my rebuilt Start AI Engineering roadmap https://github.com/louisfb01/start-ai-engineering . It is a mostly free public roadmap on GitHub, with videos, books, documentation, courses, projects, and job resources organized for the work AI engineers actually do in 2026. You can also explore the longer evergreen roadmap on this site https://www.louisbouchard.ai/learn-ai-engineering/ . Watch the complete explanation here, then use the rest of this article as the practical learning plan. AI engineering changed because the model is already there When I started teaching in 2020, many company projects followed a familiar pattern. A team had…