Applied Artificial Intelligence
Move beyond basic prompt queries to build, evaluate, and deploy dependable AI systems. Learn how retrieval-augmented generation, tool calling, and alignment mechanisms turn raw language models into reliable production tools.
Like Duolingo, but for Applied Artificial Intelligence. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
24 levels across 4 sections, about 48 minutes end to end, roughly 10 days at five minutes a day. It moves through Making Output Reliable, Connecting Models to Knowledge and Tools, Evaluating Quality and Operational Trade-Offs, and Understanding the Underlying Engine. It assumes you already know the basics.
Free forever · No credit card · iPhone & Android
Key ideas in Applied Artificial Intelligence
- Prompt instructions alone cannot strictly prevent models from outputting conversational filler text.
- Native JSON schemas constrain model token probabilities directly to guarantee valid, parseable syntax.
- System prompt warnings about JSON format often degrade under edge cases or varied user inputs.
- Few-shot examples anchor an LLM's pattern matching better than long descriptive instructions.
- Dense paragraphs of rules often suffer from prompt drift or contradictory interpretations.
- Demonstrating edge cases directly in prompt examples teaches exact syntactic structure.
- Autoregressive models predict tokens sequentially and cannot revise earlier outputs.
- Emitting reasoning steps into the context window grounds subsequent token calculations.
- Asking for the final answer first forces the model to guess before computing logic.
- Negative constraints leave undefined behavior when forbidden conditions occur.
- Explicit fallback values provide deterministic routes for edge cases.
- Clear default tokens prevent models from inventing plausible substitute facts.
- Structural delimiters like XML tags or triple quotes establish clear input boundaries.
- Clear boundaries prevent models from mistaking user input payloads for core system directives.
- Conversational trust rules do not stop adversarial prompt injection attacks.
- Monolithic prompts suffer from attention degradation across competing subtasks.
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 Applied Artificial Intelligence 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.
How do native JSON schemas guarantee machine-parseable syntax from an AI model?
Get it right to open this lesson and 23 more in the app.
Where Applied Artificial Intelligence takes you
- 1
Making Output Reliable
- Structured Output and Prompt Directives
- Designing Multi-Step AI Workflows
- 2
Connecting Models to Knowledge and Tools
- Grounding with Retrieval-Augmented Generation
- Tool Integration and Function Calling
- Choosing Between RAG and Fine-Tuning
- 3
Evaluating Quality and Operational Trade-Offs
- Benchmarking and Production Monitoring
- 4
Understanding the Underlying Engine
- Tokenization and Attention Mechanics
- Training Pipelines and Model Alignment
4 sections · 8 units · 24 levels. Built to play, not to enroll.
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
Applied Artificial Intelligence 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.
More Technology on Tomo
Working with Modern AI
Understand how contemporary artificial intelligence models actually process, reason, and generate information. Learn to steer models via structured outputs, ground them with retrieval systems, and navigate the architectural trade-offs powering modern AI applications.
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.
The Modern AI Stack
Move beyond basic prompt engineering to understand how cutting-edge AI systems actually operate. Explore the architectural shifts powering retrieval, tool-calling agents, inference scaling, and multi-step autonomy.
Practical AI and Prompt Workflows
Move beyond conversational chat to use language models as analytical engines and structured workflow partners. Learn prompt architecture, token mechanics, schema extraction, and tool integration.
AI-Native Software Engineering
Transition from using AI as a chatbot to integrating it as a core architectural component. This course covers advanced agentic workflows, automated evaluation loops, and the shift toward non-deterministic system design.
Running AI Locally
Deploy, optimize, and build with open-weight language models entirely on your own hardware. Learn how quantization, memory bandwidth, and lightweight runtimes enable private, low-latency AI workflows without reliance on cloud APIs.
Start Applied Artificial Intelligence today.
Download Tomo, search Applied Artificial Intelligence, and play your first lesson in under a minute.