Build AI Applications Fast: Complete RAG Chatbot Course for Beginners
Learn to build production-ready AI applications using modern tech stacks (Next.js, OpenAI, PostgreSQL) in this comprehensive course. No prior development experience required - start building intelligent chatbots today!
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)
Vercel AI SDK
PostgreSQL + pgvector
OpenAI API
Drizzle ORM
Vercel Platform
Program Structure
This 12-module program is designed for 4-6 hours of self-paced learning with asynchronous support via Slack/Email.Module 1: Understanding RAG Fundamentals
Module 1: Understanding RAG Fundamentals
Learn the core concepts of Retrieval-Augmented Generation and why it’s revolutionizing AI applications.
Module 2: Project Setup and Environment Configuration
Module 2: Project Setup and Environment Configuration
Set up your development environment and get the starter repository running.
Module 3: Database Schema and Data Modeling
Module 3: Database Schema and Data Modeling
Understand the database architecture and implement vector storage.
Module 4: AI SDK Integration and Embedding Generation
Module 4: AI SDK Integration and Embedding Generation
Integrate AI SDK and implement embedding generation pipelines.
Module 5: Server Actions and Data Management
Module 5: Server Actions and Data Management
Extend server actions to handle embedding generation and data management.
Module 6: Building the Chat Interface
Module 6: Building the Chat Interface
Create a responsive chat interface with real-time streaming.
Module 7: API Routes and Model Integration
Module 7: API Routes and Model Integration
Implement API routes for AI model integration and response handling.
Module 8: Tool Integration and Multi-Step Reasoning
Module 8: Tool Integration and Multi-Step Reasoning
Implement AI tools for dynamic knowledge base management.
Module 9: Semantic Search and Retrieval
Module 9: Semantic Search and Retrieval
Implement efficient semantic search algorithms and optimization.
Module 10: Production Deployment and Optimization
Module 10: Production Deployment and Optimization
Deploy your RAG agent to production and optimize for performance.
Module 11: Testing and Quality Assurance
Module 11: Testing and Quality Assurance
Implement comprehensive testing strategies for RAG applications.
Module 12: Real-World Applications and Best Practices
Module 12: Real-World Applications and Best Practices
Apply your knowledge to real-world use cases and best practices.
Prerequisites
- 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