Building RAG Question-Answering Apps with LangChain and Chroma

Retrieval-Augmented Generation (RAG) is transforming how applications extract precise answers from custom document collections. This text-based course guides you step-by-step through developing reliable question-answering tools by combining LangChain orchestration, Gemini language models, and Chroma vector storage. You will gain a solid understanding of how modern generative AI architectures work together to ground model responses in real data. Through structured explanations and clear code walkthroughs, you will progress from foundational concepts to fully functional retrieval pipelines. What you'll learn: - Understand core concepts of Retrieval-Augmented Generation, vector embeddings, and semantic search - Configure and connect Gemini language models using LangChain - Process, chunk, and index text documents into a local Chroma vector database - Build retrieval chains that combine query embedding, context fetching, and prompt assembly - Apply modern prompt engineering techniques to minimize hallucinations and cite sources accurately - Practice troubleshooting, evaluating response quality, and handling multi-document querying You will begin with essential terminology and fundamental architecture patterns before exploring document processing, embedding generation, and query execution. This course is designed for developers and AI beginners with basic Python knowledge looking to build practical information retrieval applications without prior experience in vector databases or LLM orchestration. Start reading today to build your own grounded AI question-answering workflows.