foundation-models-on-device

SKILL.md

FoundationModels: On-Device LLM (iOS 26)

Patterns for integrating Apple's on-device language model into apps using the FoundationModels framework. Covers text generation, structured output with @Generable, custom tool calling, and snapshot streaming — all running on-device for privacy and offline support.

When to Activate

Building AI-powered features using Apple Intelligence on-device

Generating or summarizing text without cloud dependency

Extracting structured data from natural language input

Implementing custom tool calling for domain-specific AI actions

Streaming structured responses for real-time UI updates

Need privacy-preserving AI (no data leaves the device)

Core Pattern — Availability Check

Always check model availability before creating a session:
struct GenerativeView: View {
    private var model = SystemLanguageModel.default

    var body: some View {
        switch model.availability {
        case .available:
            ContentView()
        case .unavailable(.deviceNotEligible):
            Text("Device not eligible for Apple Intelligence")
        case .unavailable(.appleIntelligenceNotEnabled):
            Text("Please enable Apple Intelligence in Settings")
        case .unavailable(.modelNotReady):
            Text("Model is downloading or not ready")
        case .unavailable(let other):
            Text("Model unavailable: \(other)")
        }
    }
}

Core Pattern — Basic Session

// Single-turn: create a new session each time
let session = LanguageModelSession()
let response = try await session.respond(to: "What's a good month to visit Paris?")
print(response.content)

// Multi-turn: reuse session for conversation context
let session = LanguageModelSession(instructions: """
    You are a cooking assistant.
    Provide recipe suggestions based on ingredients.
    Keep suggestions brief and practical.
    """)

let first = try await session.respond(to: "I have chicken and rice")
let followUp = try await session.respond(to: "What about a vegetarian option?")

Key points for instructions:

Define the model's role ("You are a mentor")

Specify what to do ("Help extract calendar events")

Set style preferences ("Respond as briefly as possible")

Add safety measures ("Respond with 'I can't help with that' for dangerous requests")

Core Pattern — Guided Generation with @Generable

Generate structured Swift types instead of raw strings:

1. Define a Generable Type

@Generable(description: "Basic profile information about a cat")
struct CatProfile {
    var name: String

    @Guide(description: "The age of the cat", .range(0...20))
    var age: Int

    @Guide(description: "A one sentence profile about the cat's personality")
    var profile: String
}

2. Request Structured Output

let response = try await session.respond(
    to: "Generate a cute rescue cat",
    generating: CatProfile.self
)

// Access structured fields directly
print("Name: \(response.content.name)")
print("Age: \(response.content.age)")
print("Profile: \(response.content.profile)")

Supported @Guide Constraints

.range(0...20) — numeric range

.count(3) — array element count

description: — semantic guidance for generation

Core Pattern — Tool Calling

Let the model invoke custom code for domain-specific tasks:

1. Define a Tool

struct RecipeSearchTool: Tool {
    let name = "recipe_search"
    let description = "Search for recipes matching a given term and return a list of results."

    @Generable
    struct Arguments {
        var searchTerm: String
        var numberOfResults: Int
    }

    func call(arguments: Arguments) async throws -> ToolOutput {
        let recipes = await searchRecipes(
            term: arguments.searchTerm,
            limit: arguments.numberOfResults
        )
        return .string(recipes.map { "- \($0.name): \($0.description)" }.joined(separator: "\n"))
    }
}

2. Create Session with Tools

let session = LanguageModelSession(tools: [RecipeSearchTool()])
let response = try await session.respond(to: "Find me some pasta recipes")

3. Handle Tool Errors

do {
    let answer = try await session.respond(to: "Find a recipe for tomato soup.")
} catch let error as LanguageModelSession.ToolCallError {
    print(error.tool.name)
    if case .databaseIsEmpty = error.underlyingError as? RecipeSearchToolError {
        // Handle specific tool error
    }
}

Core Pattern — Snapshot Streaming

Stream structured responses for real-time UI with PartiallyGenerated types:

@Generable
struct TripIdeas {
    @Guide(description: "Ideas for upcoming trips")
    var ideas: [String]
}

let stream = session.streamResponse(
    to: "What are some exciting trip ideas?",
    generating: TripIdeas.self
)

for try await partial in stream {
    // partial: TripIdeas.PartiallyGenerated (all properties Optional)
    print(partial)
}

SwiftUI Integration

@State private var partialResult: TripIdeas.PartiallyGenerated?
@State private var errorMessage: String?

var body: some View {
    List {
        ForEach(partialResult?.ideas ?? [], id: \.self) { idea in
            Text(idea)
        }
    }
    .overlay {
        if let errorMessage { Text(errorMessage).foregroundStyle(.red) }
    }
    .task {
        do {
            let stream = session.streamResponse(to: prompt, generating: TripIdeas.self)
            for try await partial in stream {
                partialResult = partial
            }
        } catch {
            errorMessage = error.localizedDescription
        }
    }
}

Key Design Decisions

Decision
Rationale

On-device execution
Privacy — no data leaves the device; works offline

4,096 token limit
On-device model constraint; chunk large data across sessions

Snapshot streaming (not deltas)
Structured output friendly; each snapshot is a complete partial state

@Generable macro
Compile-time safety for structured generation; auto-generates PartiallyGenerated type

Single request per session
isResponding prevents concurrent requests; create multiple sessions if needed

response.content (not .output)
Correct API — always access results via .content property

Best Practices

Always check model.availability before creating a session — handle all unavailability cases

Use instructions to guide model behavior — they take priority over prompts

Check isResponding before sending a new request — sessions handle one request at a time

Access response.content for results — not .output

Break large inputs into chunks — 4,096 token limit applies to instructions + prompt + output combined

Use @Generable for structured output — stronger guarantees than parsing raw strings

Use GenerationOptions(temperature:) to tune creativity (higher = more creative)

Monitor with Instruments — use Xcode Instruments to profile request performance

Anti-Patterns to Avoid

Creating sessions without checking model.availability first

Sending inputs exceeding the 4,096 token context window

Attempting concurrent requests on a single session

Using .output instead of .content to access response data

Parsing raw string responses when @Generable structured output would work

Building complex multi-step logic in a single prompt — break into multiple focused prompts

Assuming the model is always available — device eligibility and settings vary

When to Use

On-device text generation for privacy-sensitive apps

Structured data extraction from user input (forms, natural language commands)

AI-assisted features that must work offline

Streaming UI that progressively shows generated content

Domain-specific AI actions via tool calling (search, compute, lookup)