Agentic AI Systems with Vector DBs & RAG

Overview

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Want to build AI that learns, adapts, and evolves in real-time? The future of AI is Agentic AI. While traditional models are built on static logic, Agentic AI enables systems to learn, think, and make autonomous decisions, evolving with each interaction and adapting to new data. In this Agentic AI course, you’ll master RAG (Retrieval-Augmented Generation) and vector databases like pgVector and ChromaDB, unlocking the ability to build AI systems that dynamically retrieve data, learn from context, and make smarter decisions. You’ll gain hands-on experience creating scalable, real-time adaptive AI—critical for industries looking to stay ahead. What makes this course unique? Unlike others, it combines cutting-edge RAG tools with practical applications of pgVector and ChromaDB, giving you the power to create AI that evolves with every interaction. Perfect for AI developers, engineers, and innovators eager to shape the future of AI. Don’t just follow trends—lead them. Enroll now and start building the future of AI today!

Syllabus

Agentic Systems and AI Memory

Explore the fundamentals of agentic systems, decision loops, and AI memory, and understand why vector databases are essential for advanced AI. Set up ChromaDB and pgVector to build a RAG-powered knowledge base for your AI agents.

PGVector and RAG Engine Development

Learn to connect PostgreSQL and PGVector to your Node.js app and implement vector ingestion, search, and RAG engine logic. Test and integrate RAG tools with MCP and GPT models for real-world AI workflows.

MongoDB Integration and Schema Optimization

Integrate MongoDB with MCP, define schemas, and handle data transformations for customers and orders. Optimize your AI system for scalability, error handling, and production-ready performance.