Actualize

Cohort 1

Cohort 2

Download this free excerpt from Jay's book A Common-Sense Guide to AI Engineering. In it, you'll learn how an LLM uses tools under the hood. This knowledge is the foundation for building agents that do what you want.

Understand LLM Tools

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

CEO @ Actualize

Jay Wengrow

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

Let's Code LLM Chatbots and Agents from Scratch

Software Engineer

Systems analyst

Systems Analyst

Senior Frontend Engineer

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 Tired of scattered documentation, outdated tutorials, and frameworks that impress in demos but collapse in production?You’re not alone.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.

Gain hands-on AI Engineering proficiency by writing real code

Gain real-world intuition for working with LLMs

Reliably guide LLMs instead of getting unpredictable results.

Build retrieval-augmented assistants that can access proprietary knowledge

Use evals to systematically optimize your LLM app’s performance over time

Assemble agents that act upon the real world

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

Software Engineering

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

Note about Python

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.

Exception: Product Managers

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

Build Your First Coding Agent

CEO of Actualize, Author of "A Common-Sense Guide to AI Engineering"

Equip an LLM with the tools it needs to write code and create substantial software projects.

Transform a simple LLM into an autonomous software developer

Create a customized coding agent that adapts to your ideal workflow and code style.

Craft your own personal coding agent

Make the most of any coding agent by understanding its capabilities and limitations.

How coding agents work under the hood

Make Your AI Assistants Smarter

It can be hard to notice when AI assistants provide incorrect info. The first step is to know what LLMs aren't good at.

Common LLM hallucination failures and why they happen

With the right tools, an agent can discover accurate data and correct its own misconceptions.

How to equip an agent with tools that research and calculate

Agentic tactics can be employed in your own LLM apps and also when you use plain old ChatGPT or Claude.

Incorporate agent techniques in your own code and daily use

LLM API Foundations: Let's Build a Basic Agent

CEO of Actualize, Author of A Common-Sense Guide to AI Engineering

You'll build a mini agent with nothing more than raw Python and an LLM API.

Build an LLM-powered agent from scratch

Although LLMs do nothing more than generate text, you'll equip a model with tools that act upon the real world.

Equip an LLM with tools

By building your own agent from scratch, you'll  understand how agents really work under the hood.

Gain deeper understanding of agents

LLM API Foundations: Let's Build a Basic Chatbot

You'll dive right into to writing code that sends prompts to an LLM and processes the model's responses.

Get started writing code that uses an LLM under the hood

You'll create a tiny but working app that allows a user to talk with a powerful AI chatbot that talks like a human.

Build a functional LLM-powered chatbot

You'll build the ability for the chatbot to remember its entire conversation and continue the dialogue seamlessly.

Learn directly from Actualize