Build a Support Agent with Vercel AI SDK
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Want to build an AI agent that can automate answers to repetitive support questions? This course shows you how to build a real customer support AI agent—from retrieval to reasoning to reliable responses. Whether you're integrating AI into an existing product or launching your first agent, you'll learn a practical, modern stack that's fast to build, easy to extend, and ready for production. Use the popular Vercel AI SDK to create and ship a customer support agent that makes autonomous decisions to either answer questions based on your support docs or search the web in real time.
Instructor
Mayo Oshin
Teacher at Scrimba
Course details
22+ lessons
Interactive
Included
Subscription
What you'll learn
Explain RAG & embeddings and decide when to use each of them
Set up Supabase as a vector store: create tables, embed documents, and handle chunking/text splitting for large files
Implement retrieval with Supabase RPC so your agent can fetch the right context for any question
Use Vercel AI SDK basics: embeddings and generateText for fast, reliable model calls
Prerequisites
Basic knowledge of JavaScript/TypeScript
Familiarity with APIs, Node.js tooling, and SQL
Familiarity with prompts, RAG, and embeddings
A Supabase account and OpenAI API key
Who this course is for
Developers building AI-powered customer support systems
Engineers working with RAG and vector databases
Developers wanting to learn Vercel AI SDK
Anyone building autonomous AI agents
Curriculum
Introduction & RAG Fundamentals
3 lessons
Vector Database Setup
4 lessons
Vercel AI SDK Basics
3 lessons
Advanced AI SDK Features
3 lessons
Building the Complete Agent
6 lessons
Course Completion
3 lessons
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