Exploring AI for iOS Development

This book is a practical guide for iOS, macOS, and visionOS developers looking to integrate AI models directly into their applications using Apple's MLX Swift framework (and other frameworks soon). From Apple Intelligence to on-device models using MLX Swift, you will learn everything you need to know about working with AI on Apple platforms.

In this book, you will learn how to build on-device AI apps for Apple platforms:

MLX Swift for Different LLM/VLMs

1. Introduction to MLX

Understanding the fundamentals of the MLX framework and its role in Apple development, including battery life considerations for on-device AI

2. Understanding AI Model Components

The components that make up AI models, including licensing considerations for App Store submissions

3. Loading Models with MLX Swift

How to load various model architectures using MLX Swift, with error handling for network failures and retry strategies

4. Getting Started with MLX Swift

Setting up your development environment and running your first MLX Swift code, with realistic performance expectations for different devices

5. Working with Pre-Trained Models

Using existing open-weights models for different tasks, including memory management and testing strategies for actual devices vs simulators

6. Model Quantization

Techniques to make large AI models smaller and faster for on-device performance, and when to avoid quantization

7. Text Embeddings with MLX Embedders

Using text embedding models to understand semantic meaning of text for search and comparison, with storage for large datasets

8. Customizing Generation Parameters

Fine-tuning parameters like temperature and top-k to control generative model output, with debugging techniques and parameter logging strategies

9. Vision-Language Models

Working with models that understand and describe image content, including image preprocessing best practices and quality considerations

10. MLX Swift Tools

Utilities for MLX Swift development, including integration tips for CI/CD pipelines and automated testing

11. Tool Use with Models

Enabling language models to interact with external tools and APIs, with security considerations and input validation strategies

12. Generative Vision with Image Tool

Working with models that generate and manipulate images based on text instructions, with result evaluation techniques and failure pattern analysis

13. Structured Generation with @Generable

Using Foundation Models' schema system to get type-safe, structured responses from MLX Swift models

Foundation Models for Apple Intelligence

1. Introduction to Foundation Models

Apple's framework for accessing on-device Apple Intelligence models, including guided generation, tool calling, and stateful sessions for iOS 26.0+

2. Supported Languages and Internationalization

Working with Foundation Models across 14 supported locales, handling multilingual conversations, and session management strategies for global apps

3. Getting Started with Sessions

Setting up your first AI session and understanding model availability, error handling and different generation configuration options.

4. Structured Generation with Schemas

Creating type-safe, structured output directly from AI responses.

5. Basic Tool Use

Enabling your AI to perform actions and access real-world data. It shows a practical examples of working with a web search API to fetch latest information.

Upcoming chapters:

• Advanced Chat Patterns: Building production-ready conversation interfaces with context management
• Advanced Tool Patterns: Building sophisticated tool systems for complex AI workflows
• Safety and Best Practices: Implementing responsible AI features with proper guardrails and user protection

Use the code "STUDENT" for a discount if are you a student. Please do not abuse it.