AI Software Development: From First Prompt to Production Code
Mihail Eric
Creator of Stanford's AI Coding Course
Increase developer productivity with AI-first, production-ready workflows
Today many developers use AI, but few are truly productive with it. I created Stanford's first AI software development class, and after building a YC-backed coding company and leading AI at Amazon, I've seen how top engineers integrate AI into production workflows. My techniques have been used to train 200+ Stanford engineers and industry professionals.
My goal is simple: make you dramatically more productive writing software with AI than without it.
In this course, you'll learn practical, end-to-end workflows for using AI in real-world development, not toy examples.
We'll cover:
Building production features with AI agents using the research → plan → implement → test workflow
Configuring an AI native dev environment for your specific tech stack (IDE, code review, tool integrations, and beyond)
Setting up review and CI processes that catch AI errors, hallucinations, and slop before production
Enabling multiple agents to work together on the same codebase without conflict, accelerating software delivery and throughput
If you're ready to write better code, ship faster, and stay in control of your stack, let's get started.
What you’ll learn
Ship production features 2x faster by using coding agents across research, planning, implementation, testing, and review workflows
Set up any AI dev environment (Cursor, Claude Code, Windsurf) with custom prompting patterns optimized for your tech stack and coding style
Choose the right AI coding tools for your use case (lessons from evaluating 100+ products in the market)
Build production features using the research → plan → implement → test → review loop that handles complex software tasks
Identify which tasks coding agents handle autonomously and which need human oversight (and set up automated checks for both)
Coordinate 3+ coding agents asynchronously on the same codebase without merge conflicts or quality issues
Build your own coding agent and MCP server from scratch to understand how Cursor and Claude Code actually work
Learn how agents like Claude Code are prompted and context engineered to handle autonomous software tasks
Learn directly from Mihail
Mihail Eric
Created Stanford's first AI coding class. Former YC founder and Amazon AI lead.
Who this course is for
Engineers who want to increase their productivity with AI-generated code while making it actually production grade.
Engineering managers who want to ensure their teams aren't being left behind when it comes to the new way of developing software.
Those confused about how to develop with AI the right way (how to maintain context and manage multiple agents without compromising code)
What's included
Live sessions
Learn directly from Mihail Eric in a real-time, interactive format.
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.
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
13 lessons • 4 projects
Week 1
Start Here
Taming Coding Agents By Building Them
Week 2
Start Here
The AI Software Development Workflow
Schedule
Live sessions
3 hrs / week
This covers weekly lectures and in-class exercises.
Projects
2 hrs / week
Async content
3 hrs / week
This covers assigned readings, videos, and other learning materials.
Frequently asked questions
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$2,500
USD