► Learn AI Engineering: https://github.com/louisfb01/start-ai-engineering Learn how to become an AI engineer in 2026 with a practical roadmap covering LLM foundations, RAG, fine-tuning, workflows, agents, evals, tracing, deployment, and AI system design. Louis-François Bouchard explains how to use Codex, Claude Code, Cursor, ChatGPT, Gemini, and open-source models to learn faster without outsourcing your judgment, plus what separates a one-off AI demo from a reliable production system. Get the free, open-source Start AI Engineering roadmap: https://github.com/louisfb01/start-ai-engineering Chapters: 0:00 Hey! Tap the Thumbs Up button and Subscribe. You'll learn a lot of cool stuff, I promise. 00:20 - The Reality of AI Engineering in 2026 00:53 - Why Decision-Making Beats "Vibe Coding" 01:23 - Introducing the Free Open-Source AI Roadmap 02:02 - How the AI Job Market Has Changed (2020 vs Today) 03:03 - The Shift to System Design & Reliability 04:02 - Why Expertise Matters More Than Ever 05:01 - AI Engineering vs. Just Coding Agents 05:55 - Step 1: Building Foundational Vocabulary & Intuition 06:34 - Step 2: Learning from Books & Creating Mental Models 07:12 - Step 3: Structured Courses & Applied Learning 07:59 - Step 4: Getting "In the Mud" with Small Systems 08:36 - Step 5: Moving Beyond the Demo (Evals, Tracing, & Monitoring) 09:23 - Summary of the Learning Path 10:09 - Using AI to Learn AI (Properly) 11:30 - How to Use and Contribute to the Roadmap #ai #aiengineer #aiengineering