foundation-models

What this skill does

# Foundation Models

Integrate Apple's on-device LLM into your apps for privacy-preserving AI features.

## When This Skill Activates

- User wants AI text generation features
- User needs structured data from natural language
- User asks about prompting or LLM integration
- User wants to implement AI assistants
- User needs content summarization or extraction

## Quick Start

### 1. Check Availability

```swift
import FoundationModels

struct IntelligentView: View {
    private var model = SystemLanguageModel.default

    var body: some View {
        switch model.availability {
        case .available:
            ContentView()
        case .unavailable(.deviceNotEligible):
            UnsupportedDeviceView()
        case .unavailable(.appleIntelligenceNotEnabled):
            EnableIntelligenceView()
        case .unavailable(.modelNotReady):
            ModelDownloadingView()
        case .unavailable(let reason):
            ErrorView(reason: reason)
        }
    }
}
```

### 2. Create a Session

```swift
// Simple session
let session = LanguageModelSession()

// Session with instructions
let session = LanguageModelSession(instructions: """
    You are a helpful cooking assistant.
    Provide concise, practical advice for home cooks.
    """)
```

### 3. Generate Response

```swift
let response = try await session.respond(to: "What's a quick dinner idea?")
print(response.content)
```

## Prompt Engineering Best Practices

### The Instruction Formula

Instructions set the model's persona and constraints. They're prioritized over prompts.

```
[Role] + [Task] + [Style] + [Safety]
```

**Example:**
```swift
let instructions = """
    You are a fitness coach specializing in home workouts.
    Help users create exercise routines based on their equipment and goals.
    Keep responses under 100 words and use bullet points for exercises.
    Decline requests for medical advice and suggest consulting a doctor.
    """
```

### Instruction Components

| Component | Purpose | Example |
|-----------|---------|---------|
| **Role** | Define persona | "You are a travel expert" |
| **Task** | What to do | "Help plan itineraries" |
| **Style** | Output format | "Use bullet points, be concise" |
| **Safety** | Boundaries | "Don't provide medical advice" |

### Effective Prompts

Prompts are user inputs. Make them:

| Principle | Bad | Good |
|-----------|-----|------|
| **Specific** | "Help with cooking" | "Suggest a 30-minute vegetarian dinner" |
| **Constrained** | "Tell me about dogs" | "Describe Golden Retrievers in 3 sentences" |
| **Focused** | "I need help with many things" | "What ingredients substitute for eggs in baking?" |

### Prompt Patterns

**Question Pattern:**
```swift
let prompt = "What are three ways to reduce food waste at home?"
```

**Command Pattern:**
```swift
let prompt = "Create a weekly meal plan for a family of four, budget-friendly."
```

**Extraction Pattern:**
```swift
let prompt = """
    Extract the following from this email:
    - Sender name
    - Meeting date
    - Action items

    Email: \(emailContent)
    """
```

**Transformation Pattern:**
```swift
let prompt = "Rewrite this text to be more formal: \(casualText)"
```

## Structured Output with @Generable

Get typed Swift data instead of raw strings.

### Define Generable Types

```swift
@Generable(description: "A recipe suggestion")
struct Recipe {
    var name: String

    @Guide(description: "Cooking time in minutes", .range(5...180))
    var cookingTime: Int

    @Guide(description: "Difficulty level", .options(["Easy", "Medium", "Hard"]))
    var difficulty: String

    @Guide(description: "List of ingredients", .count(3...15))
    var ingredients: [String]

    @Guide(description: "Step-by-step instructions")
    var instructions: [String]
}
```

### @Guide Constraints

| Constraint | Use Case | Example |
|------------|----------|---------|
| `.range(min...max)` | Numeric bounds | `.range(1...100)` |
| `.options([...])` | Enum-like choices | `.options(["Low", "Medium", "High"])` |
| `.count(n)` | Exact array length | `.count(5)` |
| `.count(min...max)` | Array length range | `.count(3...10)` |

### Generate Structured Data

```swift
let session = LanguageModelSession(instructions: """
    You are a recipe assistant. Generate practical, home-cook friendly recipes.
    """)

let recipe = try await session.respond(
    to: "Suggest a quick pasta dish",
    generating: Recipe.self
)

print("Recipe: \(recipe.content.name)")
print("Time: \(recipe.content.cookingTime) minutes")
print("Ingredients: \(recipe.content.ingredients.joined(separator: ", "))")
```

### Complex Nested Structures

```swift
@Generable(description: "A travel itinerary")
struct Itinerary {
    var destination: String

    @Guide(description: "Daily activities for the trip")
    var days: [DayPlan]
}

@Generable(description: "Activities for one day")
struct DayPlan {
    var dayNumber: Int

    @Guide(description: "Morning activity")
    var morning: String

    @Guide(description: "Afternoon activity")
    var afternoon: String

    @Guide(description: "Evening activity")
    var evening: String
}
```

## Tool Calling

Let the model call your code to access data or perform actions.

### Define a Tool

```swift
struct WeatherTool: Tool {
    let name = "getWeather"
    let description = "Get current weather for a location"

    struct Arguments: Codable {
        var location: String
    }

    func call(arguments: Arguments) async throws -> ToolOutput {
        let weather = await WeatherService.shared.fetch(for: arguments.location)
        return .string("Temperature: \(weather.temp)°F, Conditions: \(weather.conditions)")
    }
}
```

### Use Tools in Session

```swift
let weatherTool = WeatherTool()
let session = LanguageModelSession(
    instructions: "You help users plan outdoor activities based on weather.",
    tools: [weatherTool]
)

// Model automatically calls tool when needed
let response = try await session.respond(
    to: "Should I go hiking in San Francisco today?"
)
```

### Tool Error Handling

```swift
do {
    let response = try await session.respond(to: prompt)
} catch let error as LanguageModelSession.ToolCallError {
    print("Tool '\(error.tool.name)' failed: \(error.underlyingError)")
} catch {
    print("Generation error: \(error)")
}
```

## Snapshot Streaming

Show responses as they generate for better UX.

### Stream to SwiftUI

```swift
@Generable
struct StoryIdea {
    var title: String

    @Guide(description: "A brief plot summary")
    var plot: String

    @Guide(description: "Main characters", .count(2...4))
    var characters: [String]
}

struct StreamingView: View {
    @State private var partial: StoryIdea.PartiallyGenerated?
    @State private var isGenerating = false

    var body: some View {
        VStack(alignment: .leading) {
            if let partial {
                if let title = partial.title {
                    Text(title).font(.headline)
                }
                if let plot = partial.plot {
                    Text(plot)
                }
                if let characters = partial.characters {
                    ForEach(characters, id: \.self) { char in
                        Text("• \(char)")
                    }
                }
            }

            Button("Generate Story Idea") {
                Task { await generateStory() }
            }
            .disabled(isGenerating)
        }
    }

    func generateStory() async {
        isGenerating = true
        defer { isGenerating = false }

        let session = LanguageModelSession()
        let stream = session.streamResponse(
            to: "Create a sci-fi story idea",
            generating: StoryIdea.self
        )

        for try await snapshot in stream {
            partial = snapshot
        }
    }
}
```

## Multi-Turn Conversations

Reuse sessions to maintain context.

```swift
@Observable
final class ChatViewModel {
    private var session: LanguageModelSess