This course contains the use of artificial intelligence.

RAG for GenAI Applications is a practical course designed to help you understand, design, and evaluate retrieval-augmented generation systems. RAG combines information retrieval with large language models so that generative AI applications can answer questions using relevant documents, organizational knowledge, and trusted data sources.

You will begin with the foundations of retrieval-augmented generation, including why RAG matters, how retrieval differs from generation, and how information moves through a complete RAG architecture. You will examine the relationship between a user query, retrieved context, prompt construction, and the final generated response.

The course then focuses on data preparation for RAG. You will learn how documents are collected, parsed, cleaned, and prepared for indexing. You will compare different chunking strategies and understand how chunk size, overlap, document structure, and semantic boundaries can influence retrieval quality. You will also learn how metadata supports filtering, organization, access control, and source tracking.

In the retrieval-design section, you will explore text embeddings, vector search, and semantic similarity. You will understand how text is converted into vector representations and how vector databases identify relevant content. The course also covers ranking, reranking, metadata filtering, and retrieval strategies that help deliver better context to a language model.

Next, you will learn how to generate answers using retrieved information. You will design prompts that clearly separate user instructions from source context and encourage the model to produce grounded AI responses. You will explore citation handling, source attribution, answer validation, and techniques for reducing hallucinations. You will also learn how a system should respond when the available documents do not contain enough information.

The final section addresses production RAG systems. You will explore evaluation methods for retrieval relevance, answer quality, faithfulness, latency, and user satisfaction. You will learn how indexing, caching, batching, monitoring, and retrieval optimization can improve system performance.

The course also examines essential AI security and governance considerations. Topics include protecting sensitive documents, enforcing permissions, preventing unauthorized retrieval, monitoring data usage, and maintaining traceability.

By the end of the course, you will understand how to build and improve generative AI applications that use enterprise knowledge, private documents, and domain-specific content. You will be prepared to design solutions such as internal knowledge assistants, customer-support agents, research tools, document-question-answering applications, and AI-powered search systems.

Whether you are a developer, AI engineer, architect, data professional, product manager, or technical learner, RAG for GenAI Applications will give you a strong foundation in embeddings, vector databases, retrieval design, grounded generation, citations, evaluation, security, and production readiness.