API integration connects a web application with external systems to access functionality or data. In generative AI applications, those systems can provide language processing, image recognition, sentiment analysis, or model access, extending what the application can do. section 1.2.1
A typical integration moves input through a supporting pipeline: prepare data, send it to an AI model or external provider, then postprocess and format the result for the user interface. The surrounding application also needs UI and conversational components, backend infrastructure, deployment, and scaling. section 1.2.1 section 1.7
Production-oriented integrations commonly account for these moving parts:
- Choose a model and provider according to the application task, data requirements, and desired capabilities. section 1.7
- Use provider abstraction to reduce dependence on a single external AI service or model. section 3.5
- Handle streamed output, asynchronous processing, connection management, buffering, and errors. section 3.5
- Separate concerns, integrate incrementally, test and validate behavior, and document the integration. section 3.5
AI web application integration
A JavaScript and React application can use Next.js with the Vercel AI SDK to interact with external AI providers. The SDK exposes generateText and streamText for generation, while useChat and useCompletion support conversational and completion interfaces. Streaming responses can improve the user experience while the SDK handles much of the underlying connection and buffering complexity. section 1.7 section 3.5
