Foundation Models

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Discuss the Foundation Models framework which provides access to Apple’s on-device large language model that powers Apple Intelligence to help you perform intelligent tasks specific to your app.

Foundation Models Documentation

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How to Ensure Controlled and Contextual Responses Using Foundation Models ?
Hi everyone, I’m currently exploring the use of Foundation models on Apple platforms to build a chatbot-style assistant within an app. While the integration part is straightforward using the new FoundationModel APIs, I’m trying to figure out how to control the assistant’s responses more tightly — particularly: Ensuring the assistant adheres to a specific tone, context, or domain (e.g. hospitality, healthcare, etc.) Preventing hallucinations or unrelated outputs Constraining responses based on app-specific rules, structured data, or recent interactions I’ve experimented with prompt, systemMessage, and few-shot examples to steer outputs, but even with carefully generated prompts, the model occasionally produces incorrect or out-of-scope responses. Additionally, when using multiple tools, I'm unsure how best to structure the setup so the model can select the correct pathway/tool and respond appropriately. Is there a recommended approach to guiding the model's decision-making when several tools or structured contexts are involved? Looking forward to hearing your thoughts or being pointed toward related WWDC sessions, Apple docs, or sample projects.
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Jul ’25
Is it possible to pass the streaming output of Foundation Models down a function chain
I am writing a custom package wrapping Foundation Models which provides a chain-of-thought with intermittent self-evaluation among other things. At first I was designing this package with the command line in mind, but after seeing how well it augments the models and makes them more intelligent I wanted to try and build a SwiftUI wrapper around the package. When I started I was using synchronous generation rather than streaming, but to give the best user experience (as I've seen in the WWDC sessions) it is necessary to provide constant feedback to the user that something is happening. I have created a super simplified example of my setup so it's easier to understand. First, there is the Reasoning conversation item, which can be converted to an XML representation which is then fed back into the model (I've found XML works best for structured input) public typealias ConversationContext = XMLDocument extension ConversationContext { public func toPlainText() -> String { return xmlString(options: [.nodePrettyPrint]) } } /// Represents a reasoning item in a conversation, which includes a title and reasoning content. /// Reasoning items are used to provide detailed explanations or justifications for certain decisions or responses within a conversation. @Generable(description: "A reasoning item in a conversation, containing content and a title.") struct ConversationReasoningItem: ConversationItem { @Guide(description: "The content of the reasoning item, which is your thinking process or explanation") public var reasoningContent: String @Guide(description: "A short summary of the reasoning content, digestible in an interface.") public var title: String @Guide(description: "Indicates whether reasoning is complete") public var done: Bool } extension ConversationReasoningItem: ConversationContextProvider { public func toContext() -> ConversationContext { // <ReasoningItem title="${title}"> // ${reasoningContent} // </ReasoningItem> let root = XMLElement(name: "ReasoningItem") root.addAttribute(XMLNode.attribute(withName: "title", stringValue: title) as! XMLNode) root.stringValue = reasoningContent return ConversationContext(rootElement: root) } } Then there is the generator, which creates a reasoning item from a user query and previously generated items: struct ReasoningItemGenerator { var instructions: String { """ <omitted for brevity> """ } func generate(from input: (String, [ConversationReasoningItem])) async throws -> sending LanguageModelSession.ResponseStream<ConversationReasoningItem> { let session = LanguageModelSession(instructions: instructions) // build the context for the reasoning item out of the user's query and the previous reasoning items let userQuery = "User's query: \(input.0)" let reasoningItemsText = input.1.map { $0.toContext().toPlainText() }.joined(separator: "\n") let context = userQuery + "\n" + reasoningItemsText let reasoningItemResponse = try await session.streamResponse( to: context, generating: ConversationReasoningItem.self) return reasoningItemResponse } } I'm not sure if returning LanguageModelSession.ResponseStream<ConversationReasoningItem> is the right move, I am just trying to imitate what session.streamResponse returns. Then there is the orchestrator, which I can't figure out. It receives the streamed ConversationReasoningItems from the Generator and is responsible for streaming those to SwiftUI later and also for evaluating each reasoning item after it is complete to see if it needs to be regenerated (to keep the model on-track). I want the users of the orchestrator to receive partially generated reasoning items as they are being generated by the generator. Later, when they finish, if the evaluation passes, the item is kept, but if it fails, the reasoning item should be removed from the stream before a new one is generated. So in-flight reasoning items should be outputted aggresively. I really am having trouble figuring this out so if someone with more knowledge about asynchronous stuff in Swift, or- even better- someone who has worked on the Foundation Models framework could point me in the right direction, that would be awesome!
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Jul ’25
"FoundationModels GenerationError error 2" on iOS 26 beta 3
Hi all, I'm working on an app that utilizes the FoundationModels found in iOS 26. I updated my phone to iOS 26 beta 3 and am now receiving the following error when trying to run code that worked in beta 2: Al Error: The operation couldn't be completed. (FoundationModels.LanguageModelSession.Genera- tionError error 2.) I admit I'm a bit of a new developer, but any idea if this is an issue with beta 3 or work that I'll need to do to adapt my code to some changes in the AI API? Thank you!
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Jul ’25
Foundation Models performance reality check - anyone else finding it slow?
Testing Foundation Models framework with a health-focused recipe generation app. The on-device approach is appealing but performance is rough. Taking 20+ seconds just to get recipe name and description. Same content from Claude API: 4 seconds. I know it's beta and on-device has different tradeoffs, but this is approaching unusable territory for real-time user experience. The streaming helps psychologically but doesn't mask the underlying latency.The privacy/cost benefits are compelling but not if users abandon the feature before it completes. Anyone else seeing similar performance? Is this expected for beta, or are there optimization techniques I'm missing?
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Jul ’25
Issue with #Playground and Foundation Model
Hi all, I’m encountering an issue when trying to run Apple Foundation Models in a blank project targeting iOS 26. Below are the details: Xcode: Latest version with iOS 26 SDK macOS: macOS 26 Tahoe (installed on main disk) Mac: 16” MacBook Pro with M2 Pro chip Apple Intelligence: Available and functional on this machine Problem: I created a new blank iOS project, set the deployment target to iOS 26, and ran the following minimal code using Foundation Models. However, I get no response at all in the output - not even an error. The app runs, but the model does not produce any output. #Playground { let session = LanguageModelSession() let response = try await session.respond(to: "Tell me a story") } Then, I tried to catch an error with this code: #Playground { let session = LanguageModelSession() do { let response = try await session.respond(to: "Tell me a story") print(response) } catch { print("Failed to get response:", error) } print("This line, never gets executed") } And got these results: I’ve done further testing and discovered something important: I tried running the Code Along sample project, and there the #Playground macro worked without issues. The only significant difference I noticed was the Canvas run destination: In my original project, I was using iPhone 16 Pro (iOS 26) as the run target in Canvas. Apple Intelligence was enabled on the simulator, but no response was returned when executing the prompt. In the sample project, the Canvas was running on My Mac. I attempted to match that setup, but at first, my destination was My Mac (Designed for iPad), which still didn’t work. The macro finally executed properly once I switched to My Mac (AppKit). So the question is ... it seems that for now, Foundation Models and the #Playground macro only run correctly when the canvas or destination is set to “My Mac (AppKit)”?
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Jul ’25
What's the best way to load adapters to try?
I'm new to Swift and was hoping the Playground would support loading adaptors. When I tried, I got a permissions error - thinking it's because it's not in the project and Playgrounds don't like going outside the project? A tutorial and some sample code would be helpful. Also some benchmarks on how long it's expected to take. Selfishly I'm on an M2 Mac Mini.
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Jul ’25
Apple's Illusion of Thinking paper and Path to Real AI Reasoning
Hey everyone I'm Manish Mehta, field CTO at Centific. I recently read Apple's white paper, The Illusion of Thinking and it got me thinking about the current state of AI reasoning. Who here has read it? The paper highlights how LLMs often rely on pattern recognition rather than genuine understanding. When faced with complex tasks, their performance can degrade significantly. I was just thinking that to move beyond this problem, we need to explore approaches that combines Deeper Reasoning Architectures for true cognitive capability with Deep Human Partnership to guide AI toward better judgment and understanding. The first part means fundamentally rewiring AI to reason. This involves advancing deeper architectures like World Models, which can build internal simulations to understand real-world scenarios , and Neurosymbolic systems, which combines neural networks with symbolic reasoning for deeper self-verification. Additionally, we need to look at deep human partnership and scalable oversight. An AI cannot learn certain things from data alone, it lacks the real-world judgment an AI will never have. Among other things, deep domain expert human partners are needed to instill this wisdom , validate the AI's entire reasoning process , build its ethical guardrails , and act as skilled adversaries to find hidden flaws before they can cause harm. What do you all think? Is this focus on a deeper partnership between advanced AI reasoning and deep human judgment the right path forward? Agree? Disagree? Thanks
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Jul ’25
Initializing session with transcript ignores tools
When I initialize a session with an existing transcript using this initializer: public convenience init(model: SystemLanguageModel = .default, guardrails: LanguageModelSession.Guardrails = .default, tools: [any Tool] = [], transcript: Transcript) The tools get ignored. I noticed that when doing that, the model never use the tools. When inspecting the transcript, I can see that the instruction entry does not have any tools available to it. I tried this for both transcripts that already include an instruction entry and ones that don't - both yielding the same result.. Is this the intended behavior / am I missing something here?
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Jul ’25
Stream response
With respond() methods, the foundation model works well enough. With streamResponse() methods, the responses are very repetitive, verbose, and messy. My app with foundation model uses more than 500 MB memory on an iPad Pro when running from Xcode. Devices supporting Apple Intelligence have at least 8GB memory. Should Apple use a bigger model (using 3 ~ 4 GB memory) for better stream responses?
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Jul ’25
Provide actionable feedback for the Foundation Models framework and the on-device LLM
We are really excited to have introduced the Foundation Models framework in WWDC25. When using the framework, you might have feedback about how it can better fit your use cases. Starting in macOS/iOS 26 Beta 4, the best way to provide feedback is to use #Playground in Xcode. To do so: In Xcode, create a playground using #Playground. Fore more information, see Running code snippets using the playground macro. Reproduce the issue by setting up a session and generating a response with your prompt. In the canvas on the right, click the thumbs-up icon to the right of the response. Follow the instructions on the pop-up window and submit your feedback by clicking Share with Apple. Another way to provide your feedback is to file a feedback report with relevant details. Specific to the Foundation Models framework, it’s super important to add the following information in your report: Language model feedback This feedback contains the session transcript, including the instructions, the prompts, the responses, etc. Without that, we can’t reason the model’s behavior, and hence can hardly take any action. Use logFeedbackAttachment(sentiment:issues:desiredOutput: ) to retrieve the feedback data of your current model session, as shown in the usage example, write the data into a file, and then attach the file to your feedback report. If you believe what you’d report is related to the system configuration, please capture a sysdiagnose and attach it to your feedback report as well. The framework is still new. Your actionable feedback helps us evolve the framework quickly, and we appreciate that. Thanks, The Foundation Models framework team
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Jul ’25
InferenceError with Apple Foundation Model – Context Length Exceeded on macOS 26.0 Beta
Hello Team, I'm currently working on a proof of concept using Apple's Foundation Model for a RAG-based chat system on my MacBook Pro with the M1 Max chip. Environment details: macOS: 26.0 Beta Xcode: 26.0 beta 2 (17A5241o) Target platform: iPad (as the iPhone simulator does not support Foundation models) While testing, even with very small input prompts to the LLM, I intermittently encounter the following error: InferenceError::inference-Failed::Failed to run inference: Context length of 4096 was exceeded during singleExtend. Has anyone else experienced this issue? Are there known limitations or workarounds for context length handling in this setup? Any insights would be appreciated. Thank you!
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Jun ’25
Foundation Models / Playgrounds Hello World - Help!
I am using Foundation Models for the first time and no response is being provided to me. Code import Playgrounds import FoundationModels #Playground { let session = LanguageModelSession() let result = try await session.respond(to: "List all the states in the USA") print(result.content) } Canvas Output What I did New file Code Canvas refreshes but nothing happens Am I missing a step or setup here? Please help. Something so basic is not working I do not know what to do. Running 40GPU, 16CPU MacBook Pro.. IOS26/Xcodebeta2/Tahoe allocated 8CPU, 48GB memory in Parallels VM. Settings for Playgrounds in Xcode Thank you for your help in advance.
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Jun ’25
How to pass data to FoundationModels with a stable identifier
For example: I have a list of to-dos, each with a unique id (a GUID). I want to feed them to the LLM model and have the model rewrite the items so they start with an action verb. I'd like to get them back and identify which rewritten item corresponds to which original item. I obviously can't compare the text, as it has changed. I've tried passing the original GUIDs in with each to-do, but the extra GUID characters pollutes the input and confuses the model. I've tried numbering them in order and adding an originalSortOrder field to my generable type, but it doesn't work reliably. Any suggestions? I could do them one at a time, but I also have a use case where I'm asking for them to be organized in sections, and while I've instructed the model not to rename anything, it still happens. It's just all very nondeterministic.
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Jun ’25
Foundation Models Error: Local Sanitizer Asset
Hi, I just upgraded to macOS Tahoe Beta 2 and now I'm getting this error when I try to initialize my Foundation Models' session: Error Resource (Local Sanitizer Asset) unavailable error. import FoundationModels #Playground { let session = LanguageModelSession() do { let result = try await session.respond(to: "Tell me 3 colors") print(result.content) } catch { print("Error", error) } } I couldn't find any resource guiding me on how to solve this. Any help/workaround? Thank you!
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Jun ’25
Safety Guardrail errors for tiny prompt (dropped into large app)
I was able to open a new project and play around with the Foundation Model, but when I dropped this class in a production app (with a lot of files) I'm running into Safety Guardrail errors for this very small prompt. Specifically it's "Safety guardrail was triggered after consecutive failures during streaming." Does it have something to do with the size of the app? I don't know what else to try to get it to work? import FoundationModels import Playgrounds @available(iOS 26.0, *) #Playground { Task { do { let session = LanguageModelSession() let prompt = "Write a short story about a talking cat." let response = try await session.respond(to: prompt) print(response) } catch { print("Error: \(error)") } } }
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Jun ’25