You've built with AI. Now ship like a professional.
A hands-on course for developers whose AI prototypes fall apart in production. Seventeen modules, one real project: you write the specs the AI reads, drive it well, harden the build, and finish with a live, deployed product and a repeatable method you'll use every day.
- One real project, end to end
- Hands-on labs every module
- Tool-agnostic method
- GitHub stars
- 85K+
- Newsletter subscribers
- 40K+
- Books on RAG, prompting and agent memory
- 3
- Core modules
- 17
The blueprint for shipping real software with AI.
The full Prompt to Production course covers the entire lifecycle: specs the AI reads, context engineering, a repeatable build method, testing and security gates, and a real deployment with CI. One continuous project carries you from the first prompt to a product in production.
What you walk away with
A real product in production
No more toy dashboard clones. Architect, secure, and deploy a live, containerized application built to survive real users and edge-case failures.
An up-to-date framework for AI development
Stop rewriting your code with every new API drop. Master the system design and methodologies that hold up no matter which LLM comes next.
Prompts that make your coding agent sing
Stop guessing and hoping. Build system prompts, context files (CLAUDE.md), and test gates that force AI agents to output production-grade code.
Run the free module on your own repository
Your coding agent starts every session from zero. The free module gives it a memory of your project: you paste one line to the agent you already use, it reads your real code, asks you five questions, and writes the decisions behind the code into files your agent reads before it writes anything. About fifteen minutes, on a repository you already have open. Free, open source, no account needed to run it.
The lab is free, and you run it on your own repository.
AGENT MEMORY CHECK: seo-autopilot Before the docs layer: 3 of 5 questions right After: 5 of 5 It was guessing about: which tools are installed vs only planned; the conventions the linter actually enforces; that packages/db is wired to nothing Contradictions found in your existing docs: 5 Written: CLAUDE.md + 4 docs, 4 unconfirmed Still advisory: nothing enforces these rules. That takes hooks, tests, CI.
More than a set of videos
The lessons and the hands-on labs, plus the people and tools to get your project shipped.
Lessons and hands-on labs
Every module is a lesson paired with a lab. You build the next piece of your own project each time, not a throwaway demo you forget by week two.
Labs that run in the coding agent you already use
The labs are written for your agent, not ours: Claude Code, Cursor, Codex, or whatever you switch to next. Each one ends in a list of acceptance criteria you tick off, so you finish knowing the thing you built actually works.
A private Discord
A members-only space to work through the material together, share what you're building, and get unstuck without waiting days for an answer.
The full course: $199
The course is live: every module, every lab, the private Discord, and lifetime access.
30 days, no questions, the refund path is automated.
The path from prompt to production
Five phases, seventeen modules, one continuous build. You begin by talking to the AI and end with a live product people can reach.
Setup & Foundations
environment · spec layer · scaffold
→ a project that boots
Build the First Version
prompting · context · MCP, agents, skills
→ features shipped fast
Research, Plan, Execute
understand · plan · verify
→ a method that scales
Harden the Build
data · tests · security
→ survives real users
Ship & Launch
Docker · CI/CD · deploy · launch
→ live behind your domain
- Part 1
Setup & Foundations
From zero to a project that boots: environment, AI tools, your project chosen and scoped, and the spec layer that drives everything after.
Module 1 · Welcome to the AI-Native World
What it means to build with AI as a professional, how the course runs, and how a lab works: you run it in your own coding agent, against criteria you tick off.
Module 2 · Your Dev Environment
Install and verify everything, then write your first CLAUDE.md, the briefing document the AI reads.
Module 3 · Choosing Your Project
Pick a real project you'll still want to open at module 16, scoped to a sane first version.
Module 4a · The Paradigm Shift
The mindset the rest of the course leans on: context is the new code, you direct an expert rather than teach a novice, the spec leads, and you build a deterministic harness around a probabilistic model.
Module 4b · Your Project's Spec Layer
Turn that mindset into files: the documents the AI reads every session, wired so nothing sits inert, and a booting spec layer before any app code.
- Part 2
Building the Project (the Vibe Phase)
Ship features fast by driving the AI well, and feel where pure vibe coding runs out of road.
Module 5 · Prompt Engineering for Developers
Get the code you want, then vibe the essentials of your app onto the scaffold.
Module 6 · From Prompt Engineering to Context Engineering
Control everything the model sees so it stops guessing, and ship your second feature with less effort.
Module 7 · Primitives: MCPs, Sub-agents, and Skills
The three building blocks the AI uses to act, delegate, and remember, and how to pick the right one.
Module 8 · Make the Runtime Enforce Your Rules
Hooks put your rules in the runtime around the model, so they hold whether or not it cooperates.
Module 9 · Minimum Friction: Making Claude Your Central Hub
Wire the AI to your files and the tools you use, so you stop leaving the terminal.
- Part 3
Research, Plan, Execute
The methodology that takes over when vibing stops scaling, plus the daily rhythm to run it.
Module 10 · Research, Plan, Execute
Understand the problem first, plan with a real acceptance test, then build in ordered chunks you verify before moving on.
Module 11 · Working with AI: Sessions, Agents, and the Daily Rhythm
How to spend your day with all of it, built around how focus and memory really work.
- Part 4
Hardening the Build
Make it survive: data, tests, code quality, and security, applied with the method from Part 3.
Module 12a · The Build That Holds Together
Pull the vibe-coded features back under spec: grade the schema the AI hands you, write tests from goals rather than from code, and meet the Iron Law of TDD.
Module 12b · The Build That Defends Itself
A small linter that heals its own violations, AI reviewers over your code, and the security contract written before the auth code.
- Part 5
Ship It
From your laptop to live with users: deploy, debug, validate, launch.
Module 13 · Deploy to Production
Containerize with Docker, set up CI/CD, and deploy to the cloud behind a real domain with a green pipeline gating every change.
Module 14a · Testing & Debugging
Debug real production issues with AI, and turn every bug you fix into a permanent regression test.
Module 14b · Validation & Security in Production
Catch the silent decisions the AI made, and defend the live project once real attackers show up.
Module 15a · Mapping a Field You Don't Yet Know (bonus)
Walk into a field that's new to you, web for a data person, RAG for a web dev, and map it before you commit to anything.
Module 15b · Prove It, Then Ship It (bonus)
Close the gap between feeling ready and proving it: a runnable spike, a real eval, then the feature shipped.
Module 16 · Launch Day
Make your project reachable to the people, agents, and crawlers that find it from outside, and ship it.
Is this for you?
You can build, but it breaks in production
You ship features fast, then they fall apart with real users. You'll learn to deploy, secure, and harden your app so it holds up once people are on it.
You're tired of chasing every new release
You want to stay ahead without relearning the tool every few months. You'll get a method that holds as the models and tools keep changing.
Your AI project is stuck in a notebook
You build RAG apps and agents that never leave your machine. You'll turn one into a deployed, secured product with your name on it.
Not for you if
- You want a no-code tool. You write real code here.
- You'd rather watch than build. The labs are the point.
- You want a one-off demo, not a method you keep using after the course.
Who's teaching this
Nir Diamant
Open-source AI guides for developers, 83K+ GitHub stars and a 40,000+ AI-dev newsletter.
Roi Halamish
Owner of several software companies, and a practitioner and thought leader applying AI development techniques.
From people taking it
“I have both Claude and Codex and I wanted to use as first Codex ... it means any framework subscription fits perfectly.”
“I went through the module and I like that you explicitly separate advisory rules from rules that become executable through hooks, tests or CI.”
And on the person teaching it
“I can see that you genuinely care about what you do and that you're authentic about your work, which makes it much easier for me to trust what I'm buying and learning from you.”
Questions people ask before buying
How long does it take?
Does it work with the agent I already use?
Can my company pay for it?
I am buying for a team.
What if it is not for me?
Ready to ship for real?
30 days, no questions, the refund path is automated.
Not sure yet? Try the first module free