Building AI Applications
Bridge the gap between raw LLM API calls and robust, production-ready software. Learn the patterns for reliable structured outputs, grounded retrieval pipelines, tool orchestration, and automated evaluation.
Like Duolingo, but for Building AI Applications. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
21 levels across 3 sections, about 42 minutes end to end, roughly 8 days at five minutes a day. It moves through Making AI Outputs Predictable and Grounded; Orchestrating Actions and Multi-Turn State; and Production Engineering, Quality, and Economics. It assumes you already know the basics.
Free forever · No credit card · iPhone & Android
Key ideas in Building AI Applications
- Prompted LLMs often add markdown code blocks or introductory commentary that breaks raw JSON parsers.
- Generative models can emit invalid escape sequences or trailing commas that violate strict JSON syntax.
- Prompt-based formatting instructions provide probabilistic compliance rather than deterministic schema guarantees.
- Constrained decoding dynamically masks logit probabilities so the model cannot sample tokens violating the grammar.
- Engine-level grammar constraints guarantee syntactically valid JSON without relying on prompt compliance.
- Grammar enforcement operates during token sampling, ensuring every emitted token matches the target JSON Schema.
- Syntactically valid JSON can still violate business logic or semantic relational constraints.
- Feeding the invalid output alongside validator error messages allows the model to self-correct edge cases.
- Grammar masks enforce structural types like numbers or strings, but cannot validate cross-field logical rules.
- Raw LLM output must be parsed and schema-validated before any UI state updates occur.
- Client fallbacks catch schema validation rejections to prevent fatal component render crashes.
- Predefined default states keep applications functional even when model outputs fail validation.
- Complex JSON schemas require grammar pre-compilation, adding measurable time-to-first-token latency.
- Smaller models guided by rigid constraints can get stuck in repetitive token loops and exhaust context limits.
- Hosted frontier model providers optimize schema enforcement engines to minimize token generation penalties.
- Embeddings cannot recover context cut in half across arbitrary chunk boundaries.
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Building AI Applications the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
When generating structured text freely, what kind of syntax slip does a model frequently make?
Get it right to open this lesson and 20 more in the app.
How to learn Building AI Applications, step by step
Go in order. 21 short levels across 3 sections, about 42 minutes in all, five minutes a day.
- 1
Making AI Outputs Predictable and Grounded
- Enforcing Reliable Structured Outputs
- Grounding Responses with Retrieval-Augmented Generation
- 2
Orchestrating Actions and Multi-Turn State
- Connecting External Tools and Function Calling
- Managing Context Windows and Conversation State
- 3
Production Engineering, Quality, and Economics
- Streaming UX and Latency Optimization
- Automated Evaluation and Regression Testing
- Model Routing and Architecture Economics
3 sections · 7 units · 21 levels. Built to play, not to enroll.
Questions about Building AI Applications
How do I learn Building AI Applications?
Go in order: start with Making AI Outputs Predictable and Grounded, then Orchestrating Actions and Multi-Turn State, then Production Engineering, Quality, and Economics. On Tomo that's 21 short levels, about 42 minutes in all, played five minutes a day. Start free and play the first level in under a minute.
Is there a Duolingo-style app for Building AI Applications?
Yes. Tomo works like Duolingo: short daily lessons, streaks and quick quizzes. Building AI Applications is one of its free courses, and you can type any other topic to get a course of your own.
Is the Building AI Applications course free?
Yes. Building AI Applications is playable on Tomo's free plan, which doesn't expire. No credit card, no waitlist. Download the app and start your first lesson.
How long does it take to learn Building AI Applications?
21 levels across 3 sections, about 42 minutes of play in total. It's built for five minutes a day, so you finish it a little at a time instead of all at once.
Do I need any background to start Building AI Applications?
No. It's pitched at an intermediate level, so it starts where you are and builds up. If you can tap a phone, you can start.
Tomo is in ChatGPT
Tomo is also a ChatGPT plugin: the same playable courses, built and played inside your chat.
Add Tomo to ChatGPT- What to ask
- “Make me a Tomo course on black holes.” “Turn this chat into a Tomo course.”
- What you get
- Quick questions with instant feedback, hearts and stars, ready to play in about a minute.
- Where it goes
- No sign-up to start. Connect your Tomo account and your streak carries on in the app.
You pick the voice
Building AI Applications is taught in the Professor style: clear, structured, thorough. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
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Start Building AI Applications today.
Download Tomo, search Building AI Applications, and play your first lesson in under a minute.