Building RAG Systems with LlamaIndex and NIM

Connecting large language models to custom documents allows you to extract precise answers and insights from private data without fine-tuning. This text-based guide teaches you how to design robust Retrieval-Augmented Generation (RAG) pipelines from the ground up using LlamaIndex and NIM inference microservices. You will begin with core architectural concepts before progressing to hands-on Python implementations. Through step-by-step written tutorials and clean code walkthroughs, you will discover how to ingest files, create vector embeddings, retrieve relevant context, and generate grounded answers. What you'll learn: - Understand foundational RAG concepts, embedding models, and vector search mechanics - Configure NIM microservice endpoints for high-throughput model inference - Parse, chunk, and index text documents using LlamaIndex data structures - Construct end-to-end query engines and chat engines for interactive document exploration - Implement semantic search and context retrieval strategies to improve response relevance - Apply evaluation patterns to measure response accuracy and prevent hallucinations The course begins with foundational terminology and architecture breakdowns, followed by clear written code examples covering data ingestion, vector indexing, and pipeline orchestration. This course is designed for developers, data practitioners, and tech enthusiasts who want to implement practical RAG solutions. Basic familiarity with Python is helpful, but no prior experience with AI frameworks or vector databases is required. Start reading today to build reliable, document-aware AI applications with confidence.