Welcome to Full-Stack RAG with Next.js, Supabase & Gemini

Course Introduction

Welcome to this comprehensive course on building production-ready Retrieval-Augmented Generation (RAG) applications. If you've been working with Next.js and Supabase and want to add powerful AI capabilities to your applications, you're in the right place.

This course takes a theory and architecture-first approach. Rather than rushing into code, we'll build a deep understanding of how RAG systems work, why they're designed the way they are, and how each component fits into the larger picture. This foundation will make you a more effective AI engineer, capable of debugging issues, optimizing performance, and making informed architectural decisions.

Who This Course Is For

This course is designed for JavaScript/Next.js developers who:

• Have working knowledge of Next.js (App Router or Pages Router)
• Are familiar with Supabase basics (authentication, database queries)
• Want to integrate AI features into their applications
• Prefer understanding concepts deeply before implementing them
• Want to build production-ready applications, not just demos

Prerequisites:

• Solid JavaScript/TypeScript fundamentals
• Experience building Next.js applications
• Basic understanding of SQL and PostgreSQL
  • Need to learn SQL? Start with SQL Basics
  • Want to master database architecture for AI? Take SQL Architecture in the AI Era
• Familiarity with REST APIs and async/await patterns

What You'll Learn

By the end of this course, you will understand:

Foundational Concepts

• What RAG is and why it's essential for modern AI applications
• How vector embeddings work and why they're the backbone of semantic search
• The complete RAG pipeline: Indexing, Retrieval, and Generation

Technical Architecture

• How to design a production RAG system using Next.js and Supabase
• Why pgvector is a game-changer for vector storage
• Server-side vs client-side concerns in AI applications

Implementation Patterns

• Document chunking strategies and their trade-offs
• Vector similarity search with cosine similarity
• Prompt engineering for grounded, accurate responses
• Streaming responses for better user experience

Production Considerations

• Security patterns including Row-Level Security for vector data
• Attribution and citation systems
• Performance optimization and cost management
• Conversational RAG with context management

Course Structure

The course is organized into five main modules:

Module 1: Foundational Theory We start with the conceptual foundation. You'll understand what RAG is, why LLMs need external memory, and how vector embeddings enable semantic search. We'll map the complete architecture to our technology stack.

Module 2: The Indexing Phase Here we dive into building the knowledge base. You'll learn document chunking strategies, vectorization processes, and how to store embeddings in Supabase with pgvector.

Module 3: The RAG Core This is where retrieval meets generation. You'll understand vector similarity search, prompt engineering for grounded responses, and how to call the Gemini API effectively.

Module 4: Production Architecture We focus on building real-world applications. You'll learn about frontend-backend communication patterns, security considerations, and how to implement attribution systems.

Module 5: Advanced Techniques Finally, we explore optimization strategies: hybrid search, conversational RAG, query transformation, and cost management.

The Technology Stack

Throughout this course, we'll work with:

Next.js - Our full-stack React framework

• Server-side rendering and API routes
• The perfect environment for secure AI operations
• Excellent developer experience with TypeScript support

Supabase - Our backend platform

• PostgreSQL database with the pgvector extension
• Row-Level Security for fine-grained access control
• Easy integration with Next.js

Google Gemini API - Our AI backbone

• Text generation with Gemini models
• Embedding generation with text-embedding-004
• Competitive pricing and excellent performance

Why This Stack?

This combination offers several advantages:

1. Integrated Data Storage: Supabase lets us store both structured data (user information, metadata) and vector data (embeddings) in the same database
2. Security Built-In: Row-Level Security policies work seamlessly with vector data
3. JavaScript Native: Everything works beautifully with TypeScript and Next.js
4. Cost Effective: Supabase's generous free tier and Gemini's competitive pricing make this accessible
5. Production Ready: These are battle-tested technologies used by thousands of production applications

Theory-First Approach

This course emphasizes understanding over copying. Here's why:

AI development is rapidly evolving. Specific APIs change, new models are released, and best practices evolve. If you only learn to copy code snippets, you'll struggle when things change. If you understand the underlying principles, you can adapt to any new tool or API.

Debugging requires understanding. When your RAG system returns irrelevant results, you need to know whether the problem is in your chunking strategy, your embedding model, your similarity search, or your prompt. Understanding the theory helps you isolate issues quickly.

Optimization requires insight. Making your system faster and cheaper requires understanding where the bottlenecks are. Theory gives you the mental model to identify and address performance issues.

Throughout each lesson, we'll present concepts theoretically first, then show how they map to our specific technologies. When you see code examples, you'll understand not just what they do, but why they're structured that way.

How to Get the Most from This Course

Read actively. Don't just skim—engage with the material. Ask yourself: "Why is this designed this way? What would happen if we did it differently?"

Build mental models. Each module introduces new concepts. Try to connect them to what you already know. How does vector similarity search relate to the database concepts you already understand?

Think about your use case. As you learn each concept, consider how it applies to applications you want to build. What kind of knowledge base would you create? What documents would you index?

Don't rush. The course is designed to be thorough. If a concept isn't clear, re-read it. Understanding the foundation makes everything else easier.

What You'll Build (Conceptually)

By the end of this course, you'll have the complete mental model and architectural blueprint for building a production-ready knowledge chatbot. This includes:

• A document ingestion pipeline that chunks and vectorizes content
• A vector database schema optimized for similarity search
• An API layer that handles retrieval and generation
• A secure, scalable architecture with proper access controls
• An attribution system that links answers to source documents
• Optimization strategies for performance and cost

The final project will challenge you to apply these concepts to a real deployment scenario.

Let's Begin

The journey from "developer who uses APIs" to "AI engineer who understands systems" starts with understanding the fundamentals. In Module 1, we'll explore what RAG really is, why it matters, and how it solves the fundamental limitations of large language models.

Ready to transform how you think about AI applications? Let's dive in.

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"The best way to predict the future is to understand it." — Adapted from Alan Kay

Proceed to Module 1: Foundational Theory