AIHero
    Free

    Series · 7 lessons

    The AI Engineer Roadmap

    Want to build AI-powered apps, but don't know where to start? You need a roadmap.

    Start lesson 1
    Matt Pocock
    Matt Pocock

    This tutorial is your roadmap to becoming an AI Engineer. We'll cover all the core concepts you need to know to build AI-powered apps.

    When you're done, you'll be able to:

    • Engage in meaningful technical discussions about AI engineering
    • Understand where new AI breakthroughs fit in the broader ecosystem
    • Make informed decisions about AI implementation strategies

    Then you'll be ready to get your hands dirty with my Vercel AI SDK tutorial, where you'll put your newfound knowledge to work.

    Let's dive into the world of AI Engineering!

    Diagram Pack

    Want to remember what we've covered? Here's a PDF of all the diagrams in the series.

    What you'll learn

    1. 01Core AI Concepts: How AI models work under the hood
    2. 02Model Selection: Choosing the right model for your specific use case
    3. 03AI Engineering Mindset: The key principles that guide successful AI projects
    4. 04AI Engineering Techniques: A directory of techniques for improving your app

    7 lessons, in order

    1. 01

      What Is An AI Engineer?

      Explore the role of an AI Engineer, learn the key skills needed, and discover how it differs from ML Engineering and AI-assisted Development.
    2. 02

      What Can You Use LLM's For?

      Discover when to use LLMs for data tasks like structuring and classification, and when to opt for deterministic systems.
    3. 03

      What Is An LLM?

      Discover the basics of Large Language Models (LLM), their structure, operation, and creation process. Perfect for beginners ready to dive into AI.
    4. 04

      5 Questions To Ask Before Choosing An LLM

      Discover the best way to choose the right LLM for your AI app. Learn about open vs closed models, cost, latency, and hosting options.
    5. 05

      The AI Engineer Mindset

      Learn how to build successful LLM applications by defining clear success metrics, and creating a data-driven culture of continuous improvement.
    6. 06

      Your App Is Only As Good As Its Evals

      How do you know if your AI application is hallucinating? How do you ensure it's outputting what you want it to? Evals are the answer.
    7. 07

      17 Techniques For Improving Your LLM-Powered App

      A directory of techniques for improving your LLM-powered app.

    After this series

    Put it to work with the skills

    Free, open-source skills that turn these fundamentals into a real engineering workflow with your agent.

    Browse the skills