AI-Native Software Engineering
Like Duolingo, but for AI-Native Software Engineering. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
For the part of you with thirty open tabs that never became anything.
A deep dive: 30 levels across 5 sections, about 60 minutes end to end, roughly 12 days at five minutes a day. It moves through The AI-Augmented Workflow; Architecting AI-Integrated Systems; Quality, Reliability, and Observability; Optimization and Infrastructure; and The Future of Engineering.
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Key ideas in AI-Native Software Engineering
- Optimizing developer productivity by prioritizing high-signal information within the LLM context window
- Distinguishing between syntactic correctness and semantic preservation in AI-driven refactors
- AI is most effective at architectural 'rubber ducking' when forced to adopt specific, conflicting personas or constraints
- AI-generated boilerplate should be treated as 'disposable'—if requirements change, it is faster to re-generate than to manually refactor
- Multi-perspective prompting reveals edge cases that a single-path 'how should I build this' prompt misses
- Manual 'polishing' of AI boilerplate creates technical debt by making the code too precious to discard
- Architectural trade-offs are best explored by asking the AI to 'steelman' the downsides of a proposed solution
- Forcing an LLM to 'think' before 'doing' reduces logic hallucinations
- Applying multi-perspective constraints to AI for architectural decision-making
- Verification of the reasoning step allows catching errors early
- Managing AI-generated boilerplate as a disposable, non-maintained asset
- Chain-of-Thought prompting is essential for non-trivial logic
- Implementing Chain-of-Thought verification for complex logic generation
- Few-shot examples provide the structural pattern for DSLs not present in the base training set
- System prompts act as a global 'linter' that persists across the entire conversation
- Providing 3-5 diverse examples of the proprietary syntax allows the model to perform in-context learning of the schema
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 AI-Native Software Engineering 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.
If an AI-refactored module compiles and passes a linter, what can you safely conclude?
Get it right to open this lesson and 29 more in the app.
Where AI-Native Software Engineering takes you
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.
- 1
The AI-Augmented Workflow
- High-Velocity Development Patterns
- Advanced Prompt Engineering for Code
- 2
Architecting AI-Integrated Systems
- RAG Architecture and Retrieval Strategy
- Agentic Workflows and Tool-Calling
- 3
Quality, Reliability, and Observability
- Automated Evaluation Frameworks
- AI-Driven Debugging and Tracing
- 4
Optimization and Infrastructure
- Model Selection and Fine-Tuning
- Performance and Cost Engineering
- 5
The Future of Engineering
- Security and Governance in the AI Stack
- The Philosophy of AI-Native Design
5 sections · 10 units · 30 levels. Built to play, not to enroll.
You pick the voice
AI-Native Software Engineering is taught in the 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 AI-Native Software Engineering today.
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