Complete Vercel AI Masterclass

Explore retrieval augmented generation (rag) and its role in supplying up-to-date data to large language models; learn how data containers, vectors, and open versus closed resources influence accuracy.

Explore how vector embeddings convert text into multi-dimensional vectors, cluster words by similarity, and store them in vector databases like Pinecone or ChromaDB to improve responses and reduce token size.

Explore Vercel AI, an open-source framework built on Next.js that lets developers integrate LLM models, RAG, and embeddings with a single Vercel AI SDK, enabling streaming generation.

Set up the Versal ai sdk to integrate multiple ai models through a single gateway endpoint, install and configure the ai package, and run a quick framework-agnostic integration.

Explore how to generate structured outputs for AI prompts, using arrays of objects with ingredients, instructions, and serve sections, and validate schemas with VersaLiAi and Zod or JSON schema.

Explore the rag workflow for document-based answering, from converting text into chunks and embeddings to storing vectors in a vector database and retrieving context with cosine similarity or euclidean distance.

Preview your app, edit code in a VS Code–style sandbox, connect to Neon for accounts, sessions, and todos, and use design mode to apply commits and UI changes with diffs.

Create a next.js project named chatbot-app, open it in VS Code, and organize code by adding a root lib folder for helper functions following best practices.

Create api/chat, api/document, and api/upload routes with route.ts, implement a post chat endpoint using streamText, system prompt, convert to model message, and set max duration to avoid vercel timeouts.

Create document upload api routes in route.ts, implementing get to list documents and delete by id using a vector store, and return structured json with next server responses.

Define upload routes for knowledge base files, validate text/plain and text/markdown up to 5 mb, extend duration to 60 seconds, and process with chunking, embedding, vector store, and sdk ui.

Build and connect a responsive chat interface using ai-sdk-react’s useChat in chat.tsx, wire to api/chat, and integrate tool calls, tailwind styling, and client-server communication.

Deploy the application on Vercel by importing the git repo and setting OpenAI keys. Verify ai features and tool calling; external hosting is needed for the vector database.