How to build scalable AI applications

This post details how the Vercel AI SDK facilitates multi-provider flexibility, enabling easy switching between AI models (e.g., OpenAI, Anthropic, Google) for optimizing speed, world knowledge, or coding expertise. It highlights the SDK's support for generative UI by streaming UI components directly from AI models and its multi-modal capabilities for processing diverse content types. The post also elaborates on advanced RAG techniques such as multi-modal RAG, dynamic RAG (real-time knowledge base updates), personalized RAG, and explainable RAG. It emphasizes Vercel's platform for simplifying AI implementation, particularly for RAG and data-intensive workflows, by integrating with Next.js data fetching patterns and Secure Compute. The post also discusses infrastructure considerations for AI applications, including latency, caching, streaming, long-running processes, scalability, memory/compute requirements, developer experience, frontend framework support, data storage/retrieval, integration, and cost, advocating for Vercel's AI-native experience over complex cloud provider configurations.