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AI for Android Developers — Fundamentals

Learn Android AI fundamentals through PocketCards: prompts, structured output, Firebase, MVVM, local storage and evidence-based evaluation.

Beginner to Intermediate12–16 hours7 modules17 available lessons

Your path, one skill at a time.

Use PocketCards as one continuous project: understand the AI boundary, inspect a runnable source revision, configure your own Firebase project, generate and review flashcards, then improve the code with tests. Six detailed lessons and practical assignments lead to a capstone. The companion codelab includes cloning, code explanations, a red/green implementation exercise and troubleshooting. Begin with the course study guide, work through the examples and exercises, and finish with the scored self-assessment and project evidence.

Suggested pace: six lesson-and-practice sessions with the 4–5 hour codelab interleaved, then 2–3 hours for the capstone and 45–60 minutes for assessment. Allow 12–16 hours overall; setup and debugging may take longer.

What you’ll learn

  • Run PocketCards from a pinned GitHub revision and explain its request/data boundaries
  • Implement and test a validation improvement without coupling UI to Firebase
  • Configure private Firebase and App Check locally, then verify a real generation
  • Demonstrate cancellation, save failure and final-card completion behavior
  • Evaluate ten source passages and document quality, privacy and usage tradeoffs

THE CURRICULUM

Build your knowledge, step by step.

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

01MODULE 01What AI means in an Android app4 lessons · 60–90 min
  • Rules, ML and generative AI
  • Training versus inference
  • The three Android AI workflows

Separate product behavior from the model that helps implement it.

  1. 01PocketCards course study guideLesson · 15–20 min
  2. 02PocketCards: browse the app source on GitHubLesson · 5 min
  3. 03What AI means in an Android appLesson · 35–50 min
  4. 04Practice: What AI means in an Android appLab · 30–60 min

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 · 60–90 min
  • 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 · 35–50 min
  2. 02Practice: Tokens, context and model behaviorLab · 30–60 min

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

03MODULE 03Choosing cloud, on-device or hybrid2 lessons · 60–90 min
  • Cloud inference
  • On-device inference
  • Hybrid routing

Make a deployment decision from requirements and measured constraints.

  1. 01Choosing cloud, on-device or hybridLesson · 35–50 min
  2. 02Practice: Choosing cloud, on-device or hybridLab · 30–60 min

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

04MODULE 04Kotlin architecture for model calls2 lessons · 60–90 min
  • 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 · 35–50 min
  2. 02Practice: Kotlin architecture for model callsLab · 30–60 min

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 · 60–90 min
  • Evaluation cases
  • Data minimization
  • Operational budgets

Measure whether the feature helps before expanding it.

  1. 01Evaluation, privacy and cost basicsLesson · 35–50 min
  2. 02Practice: Evaluation, privacy and cost basicsLab · 30–60 min

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

06MODULE 06Connecting the first AI feature3 lessons · 60–90 min
  • 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 · 35–50 min
  2. 02Practice: Connecting the first AI featureLab · 30–60 min
  3. 03PocketCards: hands-on codelabLesson · 4–5 hours

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

07MODULE 07Capstone and portfolio review2 lessons · 3–4 hours
  • 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

Extend PocketCards, prove failure behavior and submit a reproducible evaluation and portfolio.

  1. 01Capstone: AI for Android Developers — FundamentalsProject · 2–3 hours
  2. 02PocketCards final assessmentLab · 45–60 min

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.

  • Run PocketCards from a pinned GitHub revision and explain its request/data boundaries
  • Implement and test a validation improvement without coupling UI to Firebase
  • Configure private Firebase and App Check locally, then verify a real generation
  • Demonstrate cancellation, save failure and final-card completion behavior
  • Evaluate ten source passages and document quality, privacy and usage tradeoffs

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 →