Build a RAG AI Agent with Supabase (End-to-End | Chat, Update & Retrieve Data)

In this video, I walk through how to build a real RAG (Retrieval-Augmented Generation) AI Agent using Supabase — end to end.

This is not a demo or theory walkthrough.
This is a production-style implementation.

What you’ll learn in this video:
• How to connect Supabase credentials securely
• How to store and manage data for RAG
• How to retrieve data intelligently
• How to chat with your data
• How to update and fetch records dynamically
• How Supabase fits into a real AI agent architecture

This setup is ideal for:
• AI agents with memory
• Internal tools
• SaaS products
• Knowledge assistants
• Company or personal AI systems

In Part 2, I’ll go deeper into:
• Long-term memory design
• Improving retrieval quality
• Scaling the RAG system
• Making the agent more adaptive and intelligent

If you’re serious about building real AI systems — not just prompts, this video will give you a strong foundation.

— AIwithDhruv
Turning AI into outcomes.


RAG, RAGAI, RAGAgent, Supabase, SupabaseAI, VectorDatabase, AIWithSupabase, AIEngineering, AIAgents, AgenticAI, RetrievalAugmentedGeneration, AIWorkflow, Automation, AIForDevelopers, AIForBusiness, PracticalAI, AppliedAI, BuildInPublic, GenAI2026, AIArchitecture, AIInfrastructure, AIStack, SaaSAI, KnowledgeAI, AIChatbot, AIChatWithData, LLMApps, OpenAI, AIBackend, SupabaseVector, DatabaseAI, FullStackAI, AIProductDevelopment, DeveloperTutorial, AITutorial, AIwithDhruv, MemoryAI, AIWithMemory, AIDataEngineering, AISystems, IntelligentAutomation, NoHypeAI, ProductionAI