The app for independent voices
AI Engineer role is #1 on LinkedIn's fastest-growing jobs for 2026. If I had to go from zero to building agentic systems in 90 days, this is the exact learning arc I’d follow.
(PS: I saved the best for the end)
You don’t “learn AI.” You build intuition layer by layer.
LLMs are just prediction engines.
Agents are what happen when you give them context, memory, tools, and a goal.
But most people don’t know how to bridge that gap.
Here’s your roadmap
𝟬. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 (Weeks 1–3)
Understand the moving pieces before you connect them.
• Karpathy “LLMs in an Hour”
• How GPTs Are Born: Internet Feeding + Training Loops
• How GPTs Learn to Be Helpful
• Stanford CS229 Lecture
• “Attention Is All You Need”
𝟭. 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 (Weeks 4–5)
If you can’t guide the model, nothing else matters.
• OpenAI Prompt Engineering Guide
• Prompt engineering for devs
𝟮. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿: 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻 + 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 (Weeks 6–10)
Fine-tuning, retrieval, chaining, grounding, tool use.
• deeplearning.ai Generative AI Course Guide
• LLMOps: Deploy, Observe, Iterate
𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 & 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 (Weeks 11–12)
How to make agents reason, act, collaborate
• Google Agents Whitepaper
• ReAct: Reasoning + Acting
• AutoGen Patterns Short Course
• LangGraph Short Course
• GenAI_Agents GitHub repo
𝟰. 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗘𝘃𝗮𝗹𝘀 (Weeks 13–14)
If you can’t evaluate or debug your agents, you’re just playing pretend.
• Agent as a Judge
• Evaluating AI Agents
• Safe and Reliable AI via Guardrails
This is the stack I wish I had earlier.
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PS: Want a structured, hands-on path to real AI Engineering?
I’m running a 50-seat cohort for engineers serious about shipping in production. Learn alongside peers from Microsoft, Amazon, Adobe, Shopify, and more.
Details → lnkd.in/gCTA2tvp
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♻️ Restack to help more folks build the real thing