Apple Foundation Models and Image Playground

Implement reliable Apple Intelligence features with Apple's on-device text and image generation APIs across all supported platforms.

Model Limitations (CRITICAL — Read First)

The on-device model is ~3B parameters with a 4,096-token context window. It excels at summarization, extraction, classification, tagging, composition, and revision. It cannot reliably do:

• World knowledge / factual Q&A — will hallucinate
• Math and arithmetic — unreliable at multi-step calculations
• Code generation — cannot produce correct code
• Long-form writing (>200 words) — context window too small

Before writing any code, check references/model-capabilities-and-limits.md to confirm the task is within the model's capabilities. If it is not, design an escalation path (tool, backend model, or user disclosure).

Quick Reference

Workflow

1. Check model limitations first. Verify the task is within the on-device model's capabilities using the tables in references/model-capabilities-and-limits.md. If it is not, design an escalation path before writing code.
2. Classify the request. Use Foundation Models for on-device text generation, ImagePlayground for image generation, and App Intents/Shortcuts for the "Use Model" automation action.
3. Check platform support and runtime readiness. Use isAvailable and availability states, then design fallback UI for unavailable states.
4. Design instructions and prompts. Set behavioral constraints in instructions: (developer-only). Keep prompts concise with length qualifiers. See references/prompt-design-and-safety.md.
5. Design @Generable types for structured output. Use @Generable with @Guide constraints instead of asking the model to produce JSON. See references/generable-and-guided-generation.md.
6. Stream all user-facing generation. Use streamResponse(to:) instead of respond(to:) for any output the user sees. See the streaming section in references/foundation-models-framework.md.
7. Add tools only as needed. Register tools when the model needs data or actions it cannot perform alone. See references/tool-calling.md.
8. Decide whether to route to a larger model. For Apple-managed routing, use App Intents "Use Model." For in-app escalation, use an explicit backend model path. See references/routing-local-vs-bigger-model.md.
9. Validate behavior on physical devices and include robust error handling for guardrail violations, context size, language support, and tool failures.

References

• Framework core (sessions, availability, streaming, performance): references/foundation-models-framework.md
• Model capabilities and limitations: references/model-capabilities-and-limits.md
• Structured output with @Generable and @Guide: references/generable-and-guided-generation.md
• Tool calling (Tool protocol, dynamic tools, tool graphs): references/tool-calling.md
• Prompt design, safety, and error handling: references/prompt-design-and-safety.md
• Image generation (ImagePlayground, ImageCreator): references/image-playground.md
• Local vs larger-model routing strategy: references/routing-local-vs-bigger-model.md

Execution Rules

• Prefer official Apple docs and WWDC sources for API behavior.
• Treat Foundation Models app APIs as on-device unless Apple docs explicitly document a server-routing API for app code.
• Re-verify docs for "latest" requests because Apple Intelligence behavior can change across OS releases.
• Keep prompts concise and structured to reduce token use and latency.
• Check model limitations before implementing. If the task involves world knowledge, math, code generation, or long-form writing, design an escalation path — do not rely on the on-device model.
• Stream all user-facing generation. Use streamResponse(to:) for any output displayed to the user. Reserve respond(to:) for background processing.
• Use @Generable for structured output, not JSON in prompts. The model uses constrained decoding with @Generable types, guaranteeing valid output. Prompt-based JSON is unreliable.