RAG & Vector Database Courses

Ready to build production-grade AI? This program equips developers to deploy reliable generative AI solutions. We'll move past theory and focus on the proven implementation patterns you need. You'll master production essentials like model selection, cost estimation, and reliable prompt engineering to build efficient apps. You'll also implement lightweight model adaptation using PEFT. Then, you'll build end-to-end RAG systems, using vector databases to connect LLMs to your data and evaluate quality with frameworks like RAGAs. Finally, you'll dive into advanced multimodal applications that process text, images, and audio. You'll enforce structured outputs with Pydantic and implement system observability to build, trace, and debug modern AI apps.

This lesson covers vector databases, crucial for enhancing AI's long-term memory. Topics include core concepts, retrieval methods, and advanced indexing.

Build robust AI agents. Integrate tools via function calling, generate structured outputs with Pydantic, manage agent state, and utilize short-term and long-term memory. Create data-driven agents that interact with external APIs, search the web, query SQL databases, and perform agentic RAG for dynamic retrieval. Learn to evaluate agent performance for reliable, real-world applications.

Build and deploy advanced generative AI solutions on Azure with OpenAI models, GPT Vision, and DALL-E. Create RAG pipelines, craft prompts, automate workflows, and integrate multimodal AI applications.

Agentic AI Engineer with LangChain and LangGraph is a program that teaches Python developers how to turn large‑language‑model applications into fully autonomous agents.  You begin with LangChain fundamentals—prompt templates, chains, memory, and single‑tool agents—then progress to multi‑tool planning, self‑critique loops, and deployment practices. Finally, you integrate external knowledge through retrieval‑augmented generation, long‑term memory, and multi‑agent collaboration.