Master the full AI stack — generative, agentic, and synthetic — from first principles to production
A rigorous, layer-by-layer deep dive from generative AI foundations through agentic systems to synthetic data and environments — giving practitioners the full-stack mental model and hands-on skills to build, deploy, and reason about modern AI. No hype, no hand-waving: just structured mastery from the ground up.
"I built this for the engineer who's done with hand-waving — who wants the mental model, the failure modes, and the production system, in that order."— Sidney Johnson
What you'll learn
What you'll be able to do
- Explain and distinguish the generative, agentic, and synthetic layers of modern AI architecture — and know exactly when and why each layer is the right tool.
- Design and fine-tune generative models (LLMs, diffusion, multimodal) for domain-specific tasks, including prompt engineering, RAG pipelines, and parameter-efficient fine-tuning.
- Architect and deploy autonomous agentic systems with tool use, memory, planning loops, and multi-agent orchestration using frameworks like LangGraph and AutoGen.
- Generate, validate, and leverage synthetic datasets and simulated environments to train, stress-test, and evaluate AI models when real data is scarce or sensitive.
- Evaluate AI systems rigorously — measuring hallucination rates, agent reliability, and synthetic data fidelity — and apply safety and alignment best practices at each layer.
- Ship a production-ready, full-stack AI application that integrates all three layers, complete with observability, cost controls, and a documented architecture decision record.
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
6 modules · 24 lessons
Foundations: The Three-Layer Mental Model
Establish the conceptual, technical, and environmental bedrock before any code is written. Students leave with a shared vocabulary, a cost-conscious developer setup, and — critically — an evaluation-first mindset so they can measure every decision they make in later modules.
- 1.1The AI Stack Decoded: Generative, Agentic, SyntheticIncluded
- 1.2Prerequisites Checkpoint: Python, Math, and ML FluencyIncluded
- 1.3Evaluation Mindset: Measuring Before You BuildIncluded
- 1.4Developer Environment & Cost Control SetupIncluded
Generative AI: Mastering the First Layer
Build deep, practical mastery of the generative layer — from transformer internals to production RAG pipelines to multimodal diffusion systems. Every lesson pairs conceptual understanding with hands-on implementation so students can both explain and build. Evaluation touchpoints from Module 1 are applied at every stage.
- 2.1How LLMs Work: Internals Every Builder Must KnowIncluded
- 2.2Prompt Engineering as a Systems DisciplineIncluded
- 2.3RAG Pipeline Architecture: From Naive to Production-GradeIncluded
- 2.4Parameter-Efficient Fine-Tuning for Domain AdaptationIncluded
- 2.5Multimodal and Diffusion SystemsIncluded
Agentic AI: Architecting the Second Layer
Design, build, and harden autonomous agentic systems that use the generative layer as their reasoning engine. Progresses from core agent architecture through tool use and memory, to production-grade frameworks, multi-agent orchestration, and finally reliability and safety — following a deliberate complexity ramp so each lesson builds on the last.
- 3.1Agent Architecture Fundamentals: Loops, Tools, and StateIncluded
- 3.2Tool Use, Memory, and State ManagementIncluded
- 3.3Building Reliable Agents with LangGraphIncluded
- 3.4Multi-Agent Orchestration with AutoGenIncluded
- 3.5Agent Reliability, Safety, and ObservabilityIncluded
Synthetic AI: Mastering the Third Layer
Master the synthetic data layer — the infrastructure that enables AI teams to train, evaluate, and stress-test systems when real data is scarce, sensitive, or biased. Progresses from the strategic rationale and risk framework through generation techniques, to evaluation-set construction, to full simulated environments for agent training. Sequenced after the generative and agentic modules because synthetic generation and simulation both depend heavily on LLMs and agents.
- 4.1Why Synthetic: Use Cases, Risks, and the Fidelity SpectrumIncluded
- 4.2Generating Synthetic Text and Structured DataIncluded
- 4.3Synthetic Evaluation Sets and Adversarial Test DataIncluded
- 4.4Simulated Environments for Agent Training and Stress-TestingIncluded
Cross-Layer Integration: Evaluation, Safety, and Alignment
Synthesise everything learned across the three layers into a coherent production-readiness framework. Covers holistic evaluation that spans all three layers, safety and alignment practices specific to each layer and to their integration, and the observability and cost-control infrastructure needed to operate a full-stack AI system responsibly. This module is the engineering 'glue' between deep technical mastery and the capstone.
- 5.1End-to-End Evaluation Across All Three LayersIncluded
- 5.2Safety, Alignment, and Responsible AI at Each LayerIncluded
- 5.3Observability, Cost Controls, and Production ReadinessIncluded
Capstone: Full-Stack AI System in Production
Apply every skill from the academy to design, build, evaluate, harden, and ship a production-ready AI system that integrates all three layers. The capstone is structured as an accelerated product sprint with peer review gates at each phase. Students leave with a working deployed system, a documented architecture decision record, a complete eval report, and a portfolio-ready write-up.
- 6.1System Design and Architecture Decision RecordIncluded
- 6.2Build Sprint: Integrating All Three LayersIncluded
- 6.3Evaluation, Retrospective, and Portfolio PackagingIncluded
Who it's for
Is this you?
Backend engineers going AI-native
You build reliable systems for a living and want to apply that same rigor to AI — understanding the internals, not just the APIs.
Technical product managers
You need to make precise architectural decisions about AI features and stop relying on engineers to translate — this gives you the full-stack mental model to do it yourself.
ML engineers expanding their scope
You know model training but want to close the gap into agentic systems, synthetic data pipelines, and production deployment patterns.
Data scientists moving to production
You're fluent in modeling and experimentation but need the systems architecture skills to ship AI that holds up outside a notebook.
Founding engineers at AI startups
You're making stack decisions with long-term consequences and need a durable framework for evaluating trade-offs across all three AI layers.
Senior engineers reskilling for AI roles
You have the engineering depth and want to build genuine AI expertise — not surface-level familiarity, but the kind that shows in a system design conversation.
Questions
Frequently asked
Your teacher
A note from your teacher
Sidney Johnson
If you're reading this, you've probably already built something with AI — a prototype, a pipeline, maybe a feature that shipped. And if you're honest, you might feel like you're assembling pieces you don't fully understand, moving faster than your mental model can keep up with, and not entirely sure how to evaluate whether what you built is actually good.
That gap between "I can make it work" and "I understand what I built and can reason about its failure modes" is exactly what this academy is designed to close.
The AI ecosystem moves fast, and most of the learning material out there moves fast with it — tutorials built around the newest API, blog posts that are obsolete in six months, courses that teach you to call a function without explaining what happens inside it. I built The AI Layers Academy on a different premise: that if you internalize a precise, durable mental model of how the generative, agentic, and synthetic layers relate to each other and to the systems you're building, you can navigate the churn. The mental model doesn't expire when the next framework drops.
This curriculum is rigorous by design. We don't skip the internals of how LLMs work because they're inconvenient. We don't hand-wave over the hard parts of agent reliability because they're unsolved. We don't treat synthetic data as a footnote. Each layer gets a full treatment — the concepts, the failure modes, the implementation patterns, and the evaluation criteria that tell you whether what you built is actually working. Every module is grounded in code and system diagrams, not slides about trends.
The capstone is where it comes together. You'll design and ship a production-ready, full-stack AI system that integrates all three layers, with observability, cost controls, and a documented architecture decision record. It's a portfolio artifact built to withstand technical scrutiny — from a senior engineer, a system design interview, or your own rigorous second look six months from now.
If you're looking for hype or shortcuts, this isn't it. If you want to genuinely own the full AI stack — to be the person on your team who understands not just what to build, but why the architecture is right and how to know when it breaks — then let's get to work.
— Sidney Johnson
Start your journey today
Join 1 others and get instant access — learn at your own pace with an AI coach in your corner.
$5/mo
Recurring billing · cancel anytime
Enrolling a child? Sign up as a parent — you'll add your student right here after.
Secure checkout · Instant access
- 6 modules, 24 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
- Full access for as long as you're subscribed