Python LLM Development with Retrieval-Augmented AI

Build practical AI applications using Python, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG). Designed for developers who want hands-on experience creating document-aware AI systems, this course takes you from LLM API integration to building complete RAG applications. You’ll learn to integrate LLM APIs into Python applications, build backend services with FastAPI and Flask, and connect AI models to frontend interfaces. You’ll also apply strategies for handling API errors, retries, and rate limits to create more reliable AI applications. Next, you’ll explore Retrieval-Augmented Generation by working with embeddings, semantic retrieval, and vector databases including FAISS and ChromaDB. You’ll apply these concepts to develop and evaluate a document-based RAG system that retrieves relevant information and generates context-aware responses from enterprise documents. By the end of the course, you’ll be able to develop full-stack AI applications, implement semantic search with vector databases, and build document-aware RAG solutions. The course combines API development, full-stack integration, and practical RAG implementation in a hands-on workflow.