We're Back: Introducing AI Syllabus
It's time to become part of building the future
Hello brats! Happy New Year!
2025 was a whirlwind. The last time I published on tech brat was last May. I’ve been too busy building AI powered platforms and haven’t had enough time to devote to creating valuable content. It was a necessary break though, because I learned so much about AI - how it’s built, how it functions, and how to design platforms powered by it effectively.
I’ve been leading design and strategy for various Fortune 500 clients for several years now. Enterprise client problems have always been interesting to work on. You get to solve complex problems on platforms with multiple different users and user permissions. But, 2025 was the first year where conversations and strategy massively revolved around building AI systems, instead of reimagining designs on top of legacy systems.
I’ve worked on internal tools that power employee efficiency and help teams operate better together. I’ve discussed and brainstormed what a website of the future might look like. I’ve made real decisions about what AI should and shouldn’t do in healthcare, financial services, and enterprise environments. And I’ll tell you, saying “let’s just add AI” and leaving it to the “tech team” to figure it out finally isn’t accepted as a solution anymore.
You know that as a designer, I’ve been a big believer in learning how AI is actually built. We have endless content about AI trends, philosophical implications, and how to use ChatGPT for brainstorming. But, if you’ve been using a computer for the last decade or more, you won’t have trouble learning how to use a new GenAI system, whether it’s for optimizing your workflows or helping you design things.
What we don’t have enough of is practical, technical guidance for designers and tech operators who need to actually build these systems responsibly. A lot of AI education and advice for designers revolves around how to use AI to design. But, the LinkedIn influencers are wrong - learning how to use Figma Make won’t save your job in 10 years time. It’s like someone told you 10 years ago that learning Canva will make you a better website designer. It’s just a tool to help your design process, not the reason you’ll be replaced in the future.
So, tech brat is evolving.
We’re keeping the bratty opinions, but becoming more practical
I’ve decided to share the knowledge that I’ve acquired so far and that I will continue to expand this year. The cultural criticism isn’t going away, I will still be sharing essays and analysis of what’s happening in the industry. But, the past year and a half of learning about AI and operating inside the AI industry has given me a better sense of what we as designers (and other non-technical tech operators) need to know in order to be successful today and in the future. If you read the first few tech brat letters you’ll notice a lot of the links I was sharing take a bit of a technical approach to design. That’s what we will be focusing on this year.
Introducing: A 26-Week Curriculum for Designing AI
Starting next Tuesday, I’m launching a weekly AI Design syllabus that takes designers from “What even is AI?” to “I can confidently lead AI product strategy.” The weekly syllabus will basically be a lesson plan with information and resources that you will be able to follow and read in your own time.
We’ll cover practical topics that will help us design AI itself. You’ll understand how models are built and how they learn, you’ll know when to use supervised vs unsupervised learning, you’ll get familiar with designing for uncertainty and probabilistic systems, and become ready to make strategic decisions about when AI is - and isn’t - the right solution.
Here’s what we’re covering over 26 weeks:
Phase 1: Foundations (Weeks 1-8)
We start with the technical concepts every designer needs to understand before they touch an AI interface. Think the basics - answers to questions like “What is even AI?” Learning more about what type of ML is out there, understanding the difference between traditional ML, deep learning, and Generative AI, and even touch on some theory around model anatomy. Here’s a week-by-week outline to give you a better perspective:
Week 1: What Is AI? (And why it’s not just “smart software”)
Week 2: The Machine Learning Spectrum (supervised, unsupervised, reinforcement learning)
Week 3: Traditional ML vs Deep Learning vs Generative AI
Week 4: AI Model Types & What They Do
Week 5: How Models Actually Learn (Training 101)
Week 6: Large Language Models - A Deep Dive
Week 7: Model Anatomy (Parameters, Context Windows, Tokens)
Week 8: Matching Models to Problems
Phase 2: Interaction Patterns (Weeks 9-15)
After you understand how the “house is built” and the possibilities its foundation powers, we’ll focus Phase 2 on interaction patterns. We’ll see what still works and what doesn’t and come out with an understanding of why AI needs completely different design patterns compared to traditional software. Think:
Why traditional UI patterns fail for AI
Conversational interfaces (when and why)
Designing for uncertainty and confidence
Transparency and explainability
Error states and recovery
Feedback mechanisms
Building your AI pattern library
Phase 3: Enterprise & Production (Weeks 16-21)
This phase relates the concepts we’ve learned in the previous phases to more complex topics. I might run this separately for a smaller group of readers or concurrently with Phase 4, depending on what you guys find more helpful. Overall, we’ll dive into the reality of building AI at scale in regulated industries.
Topics include:
Data architecture and RAG systems
Feedback loops and learning systems
AI in regulated industries (HIPAA, GDPR, SOC2)
Human-in-the-loop design
Responsible AI frameworks
Production AI: performance, cost, scale
Phase 4: Strategy & Synthesis (Weeks 22-26)
The final phase leads us from technical knowledge to strategic leadership. We’ll learn to use everything we learned in the previous phases as a basis to strategic decision makin. We’ll work on not just executing on design challenges but becoming important figures in the rooms where companies make their business decisions. We’ll learn about:
The AI decision framework
Measuring AI product success
Designing for different AI literacy levels
The future of AI interfaces
Your complete AI design methodology
Each week will include different links, real case studies from enterprise and consumer platforms, practical exercises you can use immediately, and frameworks that help you make better decisions. I hope to also bring on guest writers that can really dive deeper into a specific topic, and offer actionable insights from their own real life examples.
Who This Is For
This curriculum is for you if:
You’re a designer who wants to lead AI initiatives, not just execute them
You’re tired of surface-level “AI trends” content and want actual technical depth
You work (or want to work) at companies building AI products
You believe we need more thoughtful, critical practitioners shaping these technologies
You want to understand AI well enough to know when to say “no, this shouldn’t use AI”
It’s also for product managers, engineers who work with designers, startup founders, and anyone who needs to make informed decisions about AI products.
What Makes This Different
Most AI education for designers and non-technical tech operators either:
Teaches you to use AI tools (ChatGPT prompts, Lovable shortcuts, etc.)
Gives high-level overviews without technical depth
Focuses on consumer apps and ignores enterprise complexity
I’m hoping to share content that teaches you to design AI systems with technical foundations, real enterprise and consumer constraints, and responsible practices built in from the start. The curriculum structure feels like a good way to get people onboard. You know what to expect and have the freedom to read and learn at your own pace.
Week 1 Syllabus Drops Next Tuesday
We’re starting with the most fundamental concept: why AI is different from every other software you’ve designed, and what that means for your entire approach
If you’re ready to understand AI deeply enough to actually shape it, what are you waiting for?
Let’s work on building something worth building, together.
- Jelena
👏🏻👏🏻look forward to the class