IOS AI Ml

iOS AI/ML

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

• Building or reviewing iOS, iPadOS, or macOS AI/ML features using Foundation Models, Core AI, Core ML, Vision, NaturalLanguage, Speech, SoundAnalysis, Create ML, Evaluations, Private Cloud Compute, or a third-party Language Model provider.
• The work includes on-device inference, custom models, multimodal prompts, agentic model tool use, AI evaluation, model-provider selection, privacy-sensitive AI, or Apple Intelligence availability gates.

Do Not Use When

• The task is plain iOS implementation with no AI/ML path; use ios-development.
• The task is AI product architecture outside Apple platforms; use ai-app-architecture, ai-llm-integration, or ai-evaluation.
• The task is only AI security, prompt injection, or action authorization; use this skill with ios-security-and-rbac.

Required Inputs

• Feature goal, model/provider choices, target Apple platforms, device/OS floor, privacy constraints, offline expectations, data inputs, tools/actions the model may call, and evaluation criteria.
• Confirm whether the deliverable is design, implementation, migration, review, evaluation plan, or release evidence.

Workflow

1. Load ios-development for Apple-platform implementation rules and availability policy.
2. Load references/apple-intelligence-stack-wwdc26.md for Foundation Models, Core AI, provider routing, Evaluations, and availability gates.
3. Load references/skill-deep-dive.md only when you need legacy Core ML, Vision, NaturalLanguage, Create ML, or model-optimization recipes.
4. Choose the lowest-risk model path that solves the task: deterministic API, Core ML/Core AI owned model, on-device Foundation Models, Private Cloud Compute, or third-party provider.
5. Define privacy, fallback, telemetry, evaluation, and security gates before writing production code.

Quality Standards

• Every AI feature has a provider boundary, availability gate, privacy path, fallback state, and evaluation set.
• On-device claims must be true: no network dependency unless the code path is explicitly cloud or third-party.
• Agentic or tool-calling model features require authorization, audit logging, and security review.
• Model performance work must include device, latency, memory, battery, and thermal evidence.

Anti-Patterns

• Treating Core ML, Core AI, and Foundation Models as interchangeable.
• Sending sensitive user data to a cloud model because the on-device path was harder.
• Shipping prompt-only tests for model behavior that changes by tool, locale, region, model, or data state.
• Logging prompts, model context, OCR text, tool payloads, or generated sensitive content without a privacy review.

Outputs

• AI feature architecture, provider decision matrix, model integration plan, evaluation suite, privacy/fallback checklist, performance budget, or review findings.

Evidence Produced

References

• references/apple-intelligence-stack-wwdc26.md for Foundation Models, Core AI, Evaluations, provider routing, availability gates, and security handoffs.
• references/skill-deep-dive.md for Core ML, Vision, NaturalLanguage, Create ML, model updates, optimization, privacy-preserving patterns, and older Apple ML recipes.

Quick Apple AI Stack Map

Inputs

Decision Rules

Capability Contract

Read and search are required; model downloads, network calls, device execution, and edits require task authority. Never upload private input merely because local inference is unavailable.

Domain Anti-Patterns

• Shipping a demo prompt as an evaluation. Fix: use versioned representative cases and failure thresholds.
• Hiding model unavailability behind a spinner. Fix: expose a deterministic fallback state.
• Letting generated output perform irreversible actions. Fix: validate and confirm structured intent.
• Logging prompts containing personal data. Fix: redact or disable payload logging.
• Claiming support from simulator results alone. Fix: test the declared physical-device matrix.