AI Software Development: From First Prompt to Production Code
Learning format
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Price
Price may change · updated within 1–2 weeks
Course language
Learning format
Subcategory
Price
Price may change · updated within 1–2 weeks
Course language
What You'll Learn
- Building your own coding agent and MCP server
- Coordinating multiple coding agents without merge conflicts
- Reducing AI hallucinations and low-quality generated code
- Applying the research-plan-implement-test-review workflow
- Choosing the right AI coding tools for your needs
- Configuring AI-native dev environments for your tech stack
Best For
- Developers wanting production-grade AI code
- Those confused about multi-agent workflows
- Engineering managers modernizing their teams
- Engineers using AI for coding
Not For
- Non-technical roles, no coding background
- People unable to attend live sessions
- Those wanting toy example tutorials
- Complete programming beginners, no experience
FAQ
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About the creator
Mihail Eric
View full profileTechnology & Software Engineering Education · Not publicly disclosed
Mihail Eric created Stanford's first AI software development course, "The Modern Software Developer." Currently Head of AI at Monaco (backed by Human Capital, Benchmark, and Founders Fund), he previously led AI at Amazon Alexa, founded a YC-backed AI coding company, and led Confetti AI, a machine learning education startup, until its 2022 acquisition.
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Reviews, Scores & Student Outcomes
Unclaimed ProfileThis course teaches engineers a full research → plan → implement → test → review workflow for shipping production-grade AI-assisted code, not toy examples. It covers configuring AI-native dev environments (Cursor, Claude Code, Windsurf), setting up review/CI processes to catch AI hallucinations and errors, and coordinating multiple coding agents on the same codebase without conflicts. Notably, it also has students build their own coding agent and MCP server from scratch to understand how these tools actually work under the hood.
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