LlamaIndex and RAG: Build Custom AI Search Applications

Large language models are powerful, but they lack access to your specific, private data. Retrieval-Augmented Generation (RAG) bridges this gap, and LlamaIndex is the premier framework for connecting your data sources to LLMs. This text-based course guides you from foundational AI concepts to deploying functional RAG pipelines. You will understand how to ingest, index, and query your data to build intelligent search and chat applications. What you'll learn: 1. Understand the core principles of Retrieval-Augmented Generation (RAG) and vector embeddings. 2. Ingest and parse diverse data sources using LlamaIndex connectors. 3. Index and store document embeddings in modern vector databases. 4. Query data efficiently using advanced retrieval strategies and post-processing. 5. Evaluate and optimize the performance of your RAG pipeline for accuracy. 6. Integrate LLMs securely to generate context-aware, precise responses. You will start with essential terminology and data concepts before progressing to hands-on pipeline configuration. The curriculum flows logically from data ingestion and indexing to advanced querying and evaluation techniques. This course is designed for software developers, data enthusiasts, and AI beginners who want to build custom data-connected applications. Basic familiarity with Python is recommended, but no prior AI experience is required. Start reading today to unlock the potential of your private data with LlamaIndex.