RAG and AI agents solve different problems. Learn how each architecture works, their key differences, when to use RAG or AI agents, and when combining both makes sense.

Learn how AI agent memory works, including short-term memory for current context, long-term memory for persistent information, and vector memory for semantic recall.

Do you really need a dedicated vector database for your AI application? Learn how PostgreSQL with pgvector handles embeddings, similarity search, RAG, metadata filtering, and when specialized vector infrastructure actually makes sense.

Learn how vector databases work from a backend developer’s perspective, including embeddings, similarity search, ANN indexes, RAG, pgvector, and when you actually need a dedicated vector database.

Learn how to build an MCP server with TypeScript, create tools, validate inputs, connect backend services, test with MCP Inspector, and prepare your server for production.

Building an AI agent demo is easy. Making it reliable, secure, observable, and safe for real users is much harder. Learn the architecture and engineering practices required to build production-ready AI agents.

What actually happens when an AI application connects to an MCP server? Learn how Model Context Protocol works, including MCP hosts, clients, servers, tools, resources, prompts, transports, security, and production architecture.