Staging environment

Let's Code LLM Chatbots and Agents from Scratch

Jay Wengrow

CEO @ Actualize

Gain hands-on AI Engineering proficiency by writing real code

Do you have a grasp of basic LLM principles but struggle to put it all into code? Tired of scattered documentation, outdated tutorials, and frameworks that impress in demos but collapse in production?

You’re not alone. Building with large language models doesn’t have to be confusing - it can be clear, structured, and deeply rewarding.

In this course, you’ll build real LLM-powered apps, one line of code at a time. You'll see how an app is built from scratch in real time, and follow along. Along the way, you'll discover not only how to implement these systems, but the why behind each line of code.

From chatbots to agents and RAG to evals, you’ll learn the core concepts of building atop LLMs. More importantly, you’ll get how LLMs “think” - allowing you to guide them to do what you want despite their nondeterministic nature. Additionally, you’ll be able to balance quality, latency, and cost, making big-picture decisions about AI-powered app architecture.

AI engineering isn’t just another branch of software development - it’s a different mindset altogether.

What you’ll learn

Build robust LLM-powered apps, chatbots, and agents. Learn by writing real code, one line at a time.

  • You'll create LLM-powered apps from scratch without needing to rely on frameworks that abstract away the important details.

  • You'll understand how LLMs work under the hood, and what they can and cannot do.

  • Understanding the essential nature of LLMs as a statistical next-token predictor is the key for utilizing them effectively.

  • LLMs are unpredictable by nature, but you'll know how to steer them into doing what you want and achieving your goals.

  • You'll use prompt and context engineering to tame the nondeterministic LLM and reduce hallucinations.

  • You'll build chatbots that converse accurately about your organization's data and help advise users appropriately.

  • You'll create your own search engine that use vector databases to power semantic search.

  • Instead of simply hoping that your newest updates make things better, you'll use evals to consistently monitor your app's quality.

  • You'll learn how to perform error analysis, annotate traces, and even automate your evals.

  • You'll equip LLMs with tools that can do more than generate text - they'll trigger real code functions

  • Your agents will write code, deploy websites, and even produce podcasts.

  • You'll gain finer control of your agent by assembling agentic workflows, thereby reducing the ways in which the agent can go rogue.

Learn directly from Jay

Jay Wengrow

Jay Wengrow

Software Engineer and Educator, Author of A Common-Sense Guide to AI Engineering

Who this course is for

  • Software engineers who want to build LLM-powered chatbots and agents, or break into AI engineering more generally

  • Data Scientists/Engineers who want to build their own LLM-powered apps

  • Product Managers who want to understand AI engineering from the coding perspective

Prerequisites

  • Software Engineering

    This course is designed for current software engineers who will use Python to build LLM-powered apps. We won't teach basic coding here.

  • Note about Python

    We'll be using Python, but we'll keep it simple in case you're more familiar with other coding languages.

  • Exception: Product Managers

    If you want an inside look at what's involved with building AI apps but don't plan on coding yourself, you'll still follow what's going on.

What's included

Jay Wengrow

Live sessions

Learn directly from Jay Wengrow in a real-time, interactive format.

Hands-on projects

Optional projects will give you the opportunity to put your newfound skills into practice

Lifetime access

Go back to course content and recordings whenever you need to.

Community of peers

Stay accountable and share insights with like-minded professionals.

Certificate of completion

Share your new skills with your employer or on LinkedIn.

Free book: A Common-Sense Guide to AI Engineering

You'll get Jay's eBook which complements the course and extends the material even further.

Maven Guarantee

This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.

Course syllabus

6 live sessions • 5 projects

Week 1

Apr 21—Apr 26

    Lesson #1: Getting Started with LLMs

    • Apr

      21

      Workshop #1: Getting Started with LLMs

      Tue 4/216:00 PM—8:00 PM (UTC)

    Lesson #2: Building a Chatbot

    • Apr

      23

      Workshop #2: Building a Chatbot

      Thu 4/236:00 PM—8:00 PM (UTC)

    Project #1

    1 item

Week 2

Apr 27—May 3

    Lesson #3: RAG, Observability, and Evals

    • Apr

      28

      Workshop #3: RAG, Observability, and Evals

      Tue 4/286:00 PM—8:00 PM (UTC)

    Lesson #4: Prompt and Context Engineering

    • Apr

      30

      Workshop #4: Prompt and Context Engineering

      Thu 4/306:00 PM—8:00 PM (UTC)

    Projects #2 and #3

    2 items

Free resources

Schedule

Live sessions

4 hrs / week

    • Tue, Apr 21

      6:00 PM—8:00 PM (UTC)

    • Thu, Apr 23

      6:00 PM—8:00 PM (UTC)

    • Tue, Apr 28

      6:00 PM—8:00 PM (UTC)

Projects

1-4 hrs / week

The homework projects are optional, but highly recommended. From proprietary chatbots to podcast-producing agents, you'll build software that is both fun and useful.

Frequently asked questions

$1,200

USD

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Apr 21May 7
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