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AI for Android Developers — Fundamentals

Understand models, prompts, inference and Android architecture before choosing an AI SDK.

Beginner to IntermediateSelf-paced7 modules13 available lessons

Your path, one skill at a time.

Understand models, prompts, inference and Android architecture before choosing an AI SDK. Follow six teaching modules, each with a practical assignment, then combine the skills in the final project. Start with the introduction even if the tools are familiar: it defines the scope and boundaries of the path.

Self-paced. Complete each exercise and use its checkpoint before moving to the next module. Revisit any prerequisite you cannot yet demonstrate.

What you’ll learn

  • Document why the feature needs a model rather than a deterministic rule
  • Show loading, cancellation, refusal and unavailable states
  • Evaluate ten representative notes and five failure cases

THE CURRICULUM

Build your knowledge, step by step.

13 lessons across 7 modules. Open a module to explore its lessons and practical work.

01MODULE 01What AI means in an Android app2 lessons · Self-paced
  • Rules, ML and generative AI
  • Training versus inference
  • The three Android AI workflows

Separate product behavior from the model that helps implement it.

  1. 01What AI means in an Android appLesson · Self-paced
  2. 02Practice: What AI means in an Android appLab · Self-paced

Ready to move on when: You can explain inference and identify a feature that should not use a model.

02MODULE 02Tokens, context and model behavior2 lessons · Self-paced
  • Tokens and context windows
  • Prompts and grounding
  • Variability and confidence

Understand what a model receives and why its output varies.

  1. 01Tokens, context and model behaviorLesson · Self-paced
  2. 02Practice: Tokens, context and model behaviorLab · Self-paced

Ready to move on when: You can distinguish formatting success from factual correctness.

03MODULE 03Choosing cloud, on-device or hybrid2 lessons · Self-paced
  • Cloud inference
  • On-device inference
  • Hybrid routing

Make a deployment decision from requirements and measured constraints.

  1. 01Choosing cloud, on-device or hybridLesson · Self-paced
  2. 02Practice: Choosing cloud, on-device or hybridLab · Self-paced

Ready to move on when: Your fallback respects the same privacy promise as the preferred route.

04MODULE 04Kotlin architecture for model calls2 lessons · Self-paced
  • Repository boundary
  • UI state and cancellation
  • Partial versus final output

Keep the UI independent from model providers and network behavior.

  1. 01Kotlin architecture for model callsLesson · Self-paced
  2. 02Practice: Kotlin architecture for model callsLab · Self-paced

Ready to move on when: You can test the ViewModel without a real model or an API key.

05MODULE 05Evaluation, privacy and cost basics2 lessons · Self-paced
  • Evaluation cases
  • Data minimization
  • Operational budgets

Measure whether the feature helps before expanding it.

  1. 01Evaluation, privacy and cost basicsLesson · Self-paced
  2. 02Practice: Evaluation, privacy and cost basicsLab · Self-paced

Ready to move on when: Your evaluation can identify a worse prompt even when its prose sounds better.

06MODULE 06Connecting the first AI feature2 lessons · Self-paced
  • Vertical slice
  • Input and result validation
  • Release readiness

Integrate one narrow capability and prove its behavior at the app boundary.

  1. 01Connecting the first AI featureLesson · Self-paced
  2. 02Practice: Connecting the first AI featureLab · Self-paced

Ready to move on when: The feature remains usable when inference is disabled.

07MODULE 07Capstone and portfolio review1 lessons · Self-paced
  • Document why the feature needs a model rather than a deterministic rule
  • Show loading, cancellation, refusal and unavailable states
  • Evaluate ten representative notes and five failure cases

Build a note assistant with a fake model first, then replace the adapter with one supported inference service.

  1. 01Capstone: AI for Android Developers — FundamentalsProject · Self-paced

Ready to move on when: Demonstrate every acceptance criterion and explain the tradeoffs without relying on the lesson text.

PROVE YOUR SKILLS

A portfolio you can build, explain, and defend.

  • Document why the feature needs a model rather than a deterministic rule
  • Show loading, cancellation, refusal and unavailable states
  • Evaluate ten representative notes and five failure cases

Use the final review rubric to identify gaps. Lesson completion tracks study progress; readiness comes from independently demonstrating the work.

Open the portfolio review rubric →