Prompt API gives web developers direct access to a browser-provided on-device AI language model. The API design offers fine-grained control, aligned with cloud API shapes, for progressively enhancing sites with model interactions tailored to individualized use cases. This complements task-based language model APIs, for example, Summarizer API, as well as a variety of APIs and frameworks for generalized on-device inference with developer-supplied ML models. The initial implementation supports text, image, and audio inputs. In addition, response constraints ensure that generated text conforms with predefined regex and JSON schema formats. This supports a variety of use cases, from generating image captions and performing visual searches to transcribing audio, classifying sound events, generating text following specific instructions, and extracting information or insights from multi-modal source material. This API has been shipped in [Chrome Extensions](https://chromewebstore.google.com/category/extensions). An enterprise policy [GenAILocalFoundationalModelSettings](https://chromeenterprise.google/policies/#GenAILocalFoundationalModelSettings) is available to disable the underlying model downloading, which would render this API unavailable. Enterprise admins can also set the [BuiltInAIAPIsEnabled](https://chromeenterprise.google/policies/#BuiltInAIAPIsEnabled) policy to block Built-In AI API usage, while still permitting other on-device GenAI features. Language support log: - Chrome M139 and earlier only supported English ('en') - Chrome M140 added support for Spanish and Japanese ('es' and 'ja')
Direct access to a language model can help web developers accomplish tasks beyond those with dedicated APIs (e.g. Summarizer API) , and tailor their usage for site-specific requirements. Compared to the low-level APIs approach (e.g a custom AI model run via WebGPU, WASM, or WebNN), using the built-in language model can save the user's bandwidth and disk space, and has a lower barrier to entry. The design offers simple shorthands for common patterns (e.g. await session.prompt(‘write a haiku’)), and supports more complex use cases for handling structured content sequences, streaming responses, availability checks, session management, and response constraints.
Samples: https://chrome.dev/web-ai-demos/prompt-api-playground https://chrome.dev/web-ai-demos/prompt-api-weather https://ai.etiennenoel.com/prompt-api https://ai.etiennenoel.com/multimodal-prompt-api https://www.clarkduvall.com/ai
Explainers: https://github.com/webmachinelearning/prompt-api/blob/main/README.md