Foundation models

Foundation Models

Integrate Apple's on-device LLM into your apps for privacy-preserving AI features. Companion references: safety-and-guardrails.md (model limits, prompt design, the four-layer safety stack), models-and-agents.md (Private Cloud Compute, LanguageModel protocol, vision input, DynamicProfile agentic sessions — the iOS 27 wave), and utilities-package.md (Apple's open-source utilities package: OpenAI-compatible endpoints, just-in-time Skills, history compression).

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 or agentic features (tool loops, multi-profile sessions)
• User needs content summarization or extraction
• User asks about Private Cloud Compute, guardrails, or model safety

Model Fit — Check Before Building

The on-device model is ~3B parameters (2-bit quantized): built for summarization, extraction, classification, tagging, revision, short chat — not math, code generation, facts, or world knowledge (WWDC25 248). For capability boundaries, prompt-design rules, and the safety stack, read safety-and-guardrails.md first. For anything bigger, PrivateCloudComputeLanguageModel (32k context, reasoning) and third-party backends are in models-and-agents.md.

Quick Start

1. Check Availability

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

// 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

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:

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:

let prompt = "What are three ways to reduce food waste at home?"

Command Pattern:

let prompt = "Create a weekly meal plan for a family of four, budget-friendly."

Extraction Pattern:

let prompt = """
    Extract the following from this email:
    - Sender name
    - Meeting date
    - Action items

    Email: \(emailContent)
    """

Transformation Pattern:

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

@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)

Two Rules the Macro Hides (WWDC25 301)

• Don't re-describe your schema in the prompt. The framework injects your @Generable type's details "in a specific format that the model has been trained on" — hand-written "respond in JSON with fields…" text duplicates it and wastes tokens. Constrained decoding masks invalid tokens per-step, so structural correctness is guaranteed, not prompted for.
• Property order is generation order. "Properties are generated in the order they are declared on your Swift struct… you may find that the model produces the best summaries when they're the last property" (WWDC25 286). Put conditioning fields (context, inputs, reasoning) before the properties that should depend on them; put summaries last. This affects output quality and streaming animations.

For schemas only known at runtime, build a DynamicGenerationSchema (supports arrayOf: and referenceTo: cross-references), validate with GenerationSchema(root:dependencies:) (throws on unresolved references), respond via session.respond(to:schema:), and read untyped values with response.content.value(String.self, forProperty: "question").

Generate Structured Data

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

@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

struct WeatherTool: Tool {
    let name = "getWeather"                              // verb, short, no abbreviations
    let description = "Get current weather for a location"  // ~one sentence

    @Generable
    struct Arguments {
        @Guide(description: "City name")
        var location: String
    }

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

Rules from the deep dive (WWDC25 301):

• Name = verb, description = one sentence. "These strings are put verbatim in your prompt. So longer strings means more tokens, which can increase the latency." No abbreviations, no implementation details.
• Arguments are @Generable — guided generation guarantees valid arguments; nest @Generable enums to give the model a closed set of options.
• The session holds one instance for its whole lifetime — tools may be stateful (e.g. track already-returned results to avoid repeats).
• Tools can be called in parallel within a single request — tool state must be concurrency-safe.
• Tool output lands in the transcript like model output — it consumes context window.

Use Tools in Session

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

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

@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.

@Observable
final class ChatViewModel {
    private var session: LanguageModelSession?
    var messages: [ChatMessage] = []

    func startConversation() {
        session = LanguageModelSession(instructions: """
            You are a helpful assistant. Remember context from earlier in our conversation.
            """)
    }

    func send(_ message: String) async throws {
        guard let session else { return }

        messages.append(ChatMessage(role: .user, content: message))

        let response = try await session.respond(to: message)

        messages.append(ChatMessage(role: .assistant, content: response.content))
    }
}

Error Handling

⚠️ LanguageModelSession.GenerationError is deprecated at iOS 27 and becomes unavailable when you rebuild with Xcode 27. This is a hard break, not a warning: Apple's deprecation note says apps built with Xcode 26 keep catching the old error until you rebuild, and you must move to the new types before submitting from Xcode 27. Its nine cases were split across three enums by concern.

do {
    let response = try await session.respond(to: prompt)

// ── LanguageModelError — model-level, backend-agnostic (any LanguageModel) ──
} catch LanguageModelError.contextSizeExceeded(let e) {
    // e.contextSize / e.tokenCount — recover by condensing the transcript (below)
} catch LanguageModelError.guardrailViolation {
    // Safety block: proactive features ignore silently;
    // user-initiated features explain + offer alternatives (see safety-and-guardrails.md)
} catch LanguageModelError.refusal(let e) {
    // NOT a safety block — the model declined for its own reasons.
    // e.explanation (and .explanationStream) say why. Surface it; don't retry blindly.
} catch LanguageModelError.rateLimited(let e) {
    // Server-backed models only. e.resetDate tells you when to retry, when known.
} catch LanguageModelError.unsupportedLanguageOrLocale {
    // Also pre-check SystemLanguageModel.default.supportedLanguages before prompting
} catch LanguageModelError.unsupportedCapability(let e) {
    // The backend doesn't do e.capability (tools / vision / reasoning / guided generation).
    // Capabilities are NOT uniform across models — see models-and-agents.md.
} catch LanguageModelError.timeout {

// ── LanguageModelSession.Error — you drove the session wrong ──
} catch LanguageModelSession.Error.concurrentRequests {
    // Gate submit on session.isResponding — one request per session
} catch LanguageModelSession.Error.transcriptMutationWhileResponding {
    // Mutate session.transcript only while isResponding == false

// ── SystemLanguageModel.Error — on-device assets ──
} catch SystemLanguageModel.Error.assetsUnavailable {
    // Model assets not on device — check availability first (see Quick Start)

} catch {
    print("Unexpected error: \(error)")
}

Migrating off GenerationError

iOS 26 GenerationError	iOS 27 replacement
exceededContextWindowSize	LanguageModelError.contextSizeExceeded — now carries contextSize and tokenCount
guardrailViolation	LanguageModelError.guardrailViolation
refusal	LanguageModelError.refusal
rateLimited	LanguageModelError.rateLimited — now carries resetDate
unsupportedGuide	LanguageModelError.unsupportedGenerationGuide (renamed)
unsupportedLanguageOrLocale	LanguageModelError.unsupportedLanguageOrLocale
concurrentRequests	LanguageModelSession.Error.concurrentRequests (moved)
assetsUnavailable	SystemLanguageModel.Error.assetsUnavailable (moved)
decodingFailure	No documented successor — don't assume one; keep a catch-all

New in iOS 27 with no iOS 26 equivalent: LanguageModelError.timeout, .unsupportedCapability, .unsupportedTranscriptContent, and LanguageModelSession.Error.transcriptMutationWhileResponding.

❌ There is no .cancelled case — on any of these enums, in any OS version. Cancellation surfaces as Swift's CancellationError, so catch is CancellationError (or just let it propagate); don't write a catch GenerationError.cancelled, which never compiled.

Context-Overflow Recovery: Condense, Don't Discard (WWDC25 301)

Apple's shown heuristic keeps the first transcript entry (the instructions — dropping it silently drops your persona and safety wording) plus the last entry:

private func newSession(previousSession: LanguageModelSession) -> LanguageModelSession {
    let allEntries = previousSession.transcript.entries
    var condensed = [Transcript.Entry]()
    if let first = allEntries.first {
        condensed.append(first)                          // instructions live here
        if allEntries.count > 1, let last = allEntries.last {
            condensed.append(last)
        }
    }
    return LanguageModelSession(transcript: Transcript(entries: condensed))
}

Context Window Management

Get Context Size Programmatically

Don't hardcode 4096 — query the model at runtime:

let model = SystemLanguageModel.default
let contextSize = try await model.contextSize
print("Context size: \(contextSize)")
// 4,096 on the first-generation model; 8,192 on the rebuilt iOS 27 on-device model;
// 32,000 on PrivateCloudComputeLanguageModel (WWDC26 241) — always query, never hardcode

contextSize is marked @backDeployed(before: iOS 26.4, macOS 26.4, visionOS 26.4).

Measure Token Usage at Runtime

Use tokenUsage(for:) to measure the exact token cost of each component instead of guessing:

let model = SystemLanguageModel.default

// Measure instruction cost
let instructions = Instructions("You're a helpful assistant that generates haiku.")
let instructionTokens = try await model.tokenUsage(for: instructions)
print(instructionTokens.tokenCount) // 16

// Measure instructions + tools combined
let tools = [MoodTool()]
let combinedTokens = try await model.tokenUsage(for: instructions, tools: tools)
print(combinedTokens.tokenCount) // 79 — tools add significant overhead!

// Measure a prompt
let prompt = Prompt("Generate a haiku about Swift")
let promptTokens = try await model.tokenUsage(for: prompt)
print(promptTokens.tokenCount) // 14

// Track cumulative usage in a multi-turn session
let session = LanguageModelSession(model: model, tools: tools, instructions: instructions)
let response = try await session.respond(to: prompt)
let transcriptTokens = try await model.tokenUsage(for: session.transcript)
print(transcriptTokens.tokenCount)

⚠️ Tools Multiply Token Consumption

Tool definitions are serialized as JSON schemas, which significantly inflates token usage. In the example above, adding a single tool jumped instructions from 16 → 79 tokens (nearly 5x). Always measure before adding tools, and consider whether you truly need them for a given session.

Token Budget Monitoring

Track context usage as a percentage to prevent overflows:

extension SystemLanguageModel.TokenUsage {
    func percent(ofContextSize contextSize: Int) -> Float {
        guard contextSize > 0 else { return 0 }
        return Float(tokenCount) / Float(contextSize)
    }

    func formattedPercent(ofContextSize contextSize: Int) -> String {
        percent(ofContextSize: contextSize)
            .formatted(.percent.precision(.fractionLength(0)).rounded(rule: .down))
    }
}

// Usage
let contextSize = try await model.contextSize
print(instructionTokens.formattedPercent(ofContextSize: contextSize)) // "0%"
print(combinedTokens.formattedPercent(ofContextSize: contextSize))    // "1%"

Pre-flight Token Budget Check

Check if a prompt fits before sending to prevent exceededContextWindowSize errors:

func canFit(prompt: Prompt, in session: LanguageModelSession, model: SystemLanguageModel) async throws -> Bool {
    let contextSize = try await model.contextSize
    let transcriptTokens = try await model.tokenUsage(for: session.transcript)
    let promptTokens = try await model.tokenUsage(for: prompt)
    let totalNeeded = transcriptTokens.tokenCount + promptTokens.tokenCount
    return totalNeeded < contextSize
}

Debug with TranscriptDebugMenu

During development, drop TranscriptDebugMenu into your SwiftUI view hierarchy to visually inspect the full conversation transcript and token consumption in real time.

Strategies for Large Content

// Break content into chunks
func processLargeDocument(_ document: String) async throws -> [Summary] {
    let chunks = document.split(every: 10000) // ~2500 tokens per chunk
    var summaries: [Summary] = []

    for chunk in chunks {
        let session = LanguageModelSession() // New session per chunk
        let summary = try await session.respond(
            to: "Summarize this section: \(chunk)",
            generating: Summary.self
        )
        summaries.append(summary.content)
    }

    return summaries
}

Generation Options

// Deterministic output: greedy sampling, not temperature 0
let response = try await session.respond(to: prompt,
    options: GenerationOptions(samplingMode: .greedy))

// Variance dial (scale runs to 2.0 — WWDC25 301)
let lowVariance  = GenerationOptions(temperature: 0.5)
let highVariance = GenerationOptions(temperature: 2.0)

⚠️ The sampling: label — GenerationOptions(sampling:) and the sampling property — is deprecated; use samplingMode. Same type, same .greedy / .random(top:seed:) / .random(probabilityThreshold:seed:) values, new name.

Setting	Use Case
samplingMode: .greedy	Deterministic outputs (tests, caching) — deterministic only per model version; OS model updates change outputs
0.3-0.7	Factual extraction, balanced tasks
1.0-2.0	Creative writing, brainstorming

Specialized Use Cases & Dev Tooling (WWDC25 286)

• Content tagging adapter: SystemLanguageModel(useCase: .contentTagging) — specialized for tag/topic/action/emotion extraction; combine with @Generable results and @Guide(.maximumCount(3)); works with custom instructions.
• Custom adapters: trainable with Apple's adapter-training toolkit, but Apple's caveat — it "requires responsibility to retrain as Apple improves the base model." Reach for prompt engineering + guided generation first.
• #Playground macro: prompt the model from any Swift file in your project and see responses in the Xcode canvas (with access to your project's @Generable types) — the fastest prompt-iteration loop.
• Instruments: the Foundation Models profiling template breaks down request latency — "understanding where latency goes can help you tweak the verbosity of your prompts, or determine when to call useful APIs such as prewarming" (session.prewarm() before the user's first request).
• Gate submit buttons on session.isResponding — one request per session at a time.

Common Patterns

Content Extraction

@Generable
struct EventDetails {
    var title: String
    var date: String?
    var time: String?
    var location: String?
    var attendees: [String]
}

let session = LanguageModelSession(instructions: """
    Extract event details from text. Use nil for missing information.
    """)

let event = try await session.respond(
    to: "Extract: Team lunch next Friday at noon in Conference Room B with John and Sarah",
    generating: EventDetails.self
)

Summarization

let session = LanguageModelSession(instructions: """
    Summarize text concisely. Focus on key points and actionable items.
    Maximum 3 bullet points.
    """)

let response = try await session.respond(
    to: "Summarize this email: \(longEmail)"
)

Classification

@Generable
struct Classification {
    @Guide(description: "Category", .options(["Bug", "Feature", "Question", "Other"]))
    var category: String

    @Guide(description: "Priority", .options(["Low", "Medium", "High"]))
    var priority: String

    @Guide(description: "Brief summary")
    var summary: String
}

let result = try await session.respond(
    to: "Classify this support ticket: \(ticketText)",
    generating: Classification.self
)

Checklist

Before shipping:

• Check model availability before showing AI features
• Provide fallback UI for unsupported devices
• Handle all error cases gracefully
• Test with various prompt lengths
• Verify context window limits aren't exceeded
• Use @Generable for any structured output
• Add appropriate loading states for generation
• Test streaming UX feels responsive
• Ensure instructions are clear and specific
• Include safety boundaries in instructions
• Never interpolate user input into instructions (prompts only — see safety-and-guardrails.md)
• Conditioning properties declared before dependent ones; summaries last
• Guardrail errors handled per feature type (silent for proactive, explained for user-initiated)
• Right model per feature (on-device vs Private Cloud Compute vs third-party — see models-and-agents.md)

References

• safety-and-guardrails.md (this skill) — model limits, prompt design, guardrails, Swiss-cheese safety stack, evals
• models-and-agents.md (this skill) — PCC, LanguageModel protocol, capabilities, vision in/out, custom segments, DynamicProfile agents, tool-calling modes, KV-cache rules
• utilities-package.md (this skill) — apple/foundation-models-utilities: chat-completions endpoints, JIT Skills, history-compression modifiers
• WWDC25 — Meet the Foundation Models framework
• WWDC25 — Deep dive into the Foundation Models framework
• WWDC26 — What's new in the Foundation Models framework
• Foundation Models Documentation
• Generating content and performing tasks
• Guided generation
• Tool calling
• HIG: Generative AI
• TN3193: Managing the on-device foundation model's context window
• Tracking token usage in Foundation Models — Artem Novichkov