Skip to main content

​
What You’ll Build

Throughout this program, you’ll build a production-ready RAG agent using the Vercel AI SDK RAG Starter repository. You’ll learn to ingest documents, create vector embeddings, implement semantic search, and generate contextually relevant responses using OpenAI’s models.

​
Learning Outcomes

  • Build production-ready RAG agents using Vercel AI SDK and Next.js 14
  • Understand vector embeddings and semantic search with practical implementations
  • Implement document ingestion and processing pipelines with automatic embedding generation
  • Create responsive chat interfaces with real-time streaming and tool visualization
  • Deploy and scale AI applications on Vercel with PostgreSQL and pgvector
  • Build AI tool integration for dynamic knowledge base management

​
Tech Stack You’ll Master

Next.js 14 (App Router)

Modern React framework with server components and app router architecture.

Vercel AI SDK

Unified AI integration toolkit for building AI-powered applications.

PostgreSQL + pgvector

Vector database storage with efficient similarity search capabilities.

OpenAI API

State-of-the-art language models for text generation and embeddings.

Drizzle ORM

Type-safe database toolkit with excellent TypeScript support.

Vercel Platform

Deployment and hosting platform with seamless database integration.

​
Program Structure

This 5-module program is designed for 4-6 hours of self-paced learning with asynchronous support via Slack/Email.

Learn the core concepts of Retrieval-Augmented Generation and why it’s revolutionizing AI applications.

  • What is RAG and Why It Matters
  • Embeddings Deep Dive: Vector Space and Similarity
  • Chunking Strategies and Best Practices
  • Practical Exercise: Embedding Exploration

    • Set up your development environment and get the starter repository running.

  • Repository Setup and Dependencies
  • PostgreSQL Database Setup with pgvector
  • Development Environment and Project Structure
  • Environment Configuration and API Keys

    • Understand the database architecture and implement vector storage.

  • Schema Analysis and Design
  • Vector Database Implementation
  • Migration and Validation
  • Performance Optimization

    • Create a streaming chat interface and set up API routes for your RAG application.

  • Chat Interface with useChat Hook
  • API Routes and System Prompts
  • Alternative AI Providers with Free Credits
  • Testing the Complete Chat Flow

    • Implement tools to add resources to your knowledge base and retrieve information for answering questions.

  • Adding Resources Tool
  • Multi-Step Tool Calls
  • Retrieving Information Tool
  • Complete RAG Functionality

    • ​
    Prerequisites

    This program assumes you have basic knowledge of React and TypeScript, but no prior experience with Next.js, Vercel, or AI development is required.
    • React & TypeScript: Basic understanding of React components and TypeScript syntax
    • Command Line: Familiarity with terminal commands and package managers
    • REST APIs: Understanding of HTTP requests and API concepts
    • Git: Basic knowledge of version control and repository management

    ​
    Support and Resources

    Slack Community

    Join our community for peer support and discussions.

    Reference & Tutorials

    Reference and tutorials classified by topics.
    The final project involves building a complete RAG application for a specific domain, showcasing all the skills learned throughout the program.
    ⌘I