axiom-foundation-models

Foundation Models — On-Device AI for Apple Platforms

When to Use This Skill

Use when:

• Implementing on-device AI features with Foundation Models
• Adding text summarization, classification, or extraction capabilities
• Creating structured output from LLM responses
• Building tool-calling patterns for external data integration
• Streaming generated content for better UX
• Debugging Foundation Models issues (context overflow, slow generation, wrong output)
• Deciding between Foundation Models vs server LLMs (ChatGPT, Claude, etc.)

Related Skills

• Use axiom-foundation-models-diag for systematic troubleshooting (context exceeded, guardrail violations, availability problems)
• Use axiom-foundation-models-ref for complete API reference with all WWDC code examples

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Red Flags — Anti-Patterns That Will Fail

❌ Using for World Knowledge

Why it fails: The on-device model is 3 billion parameters, optimized for summarization, extraction, classification — NOT world knowledge or complex reasoning.

Example of wrong use:

// ❌ BAD - Asking for world knowledge
let session = LanguageModelSession()
let response = try await session.respond(to: "What's the capital of France?")

Why: Model will hallucinate or give low-quality answers. It's trained for content generation, not encyclopedic knowledge.

Correct approach: Use server LLMs (ChatGPT, Claude) for world knowledge, or provide factual data through Tool calling.

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❌ Blocking Main Thread

Why it fails: session.respond() is async but if called synchronously on main thread, freezes UI for seconds.

Example of wrong use:

// ❌ BAD - Blocking main thread
Button("Generate") {
    let response = try await session.respond(to: prompt) // UI frozen!
}

Why: Generation takes 1-5 seconds. User sees frozen app, bad reviews follow.

Correct approach:

// ✅ GOOD - Async on background
Button("Generate") {
    Task {
        let response = try await session.respond(to: prompt)
        // Update UI with response
    }
}