Uploaded August 2026 | Updated September 2026, 2 hours ago
► Learn AI Engineering: 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: 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
► Learn AI Engineering: 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: 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
