The Retrieval-Augmented-Generation (RAG) course provides in-depth, hands-on training on building intelligent AI systems that combine large language models with external knowledge sources for accurate, context-aware responses. Delivered through Jast Tech, this program focuses on overcoming LLM limitations such as hallucinations, static knowledge, and lack of domain context by integrating document retrieval pipelines. Learners will explore embeddings, vector databases, semantic search, chunking strategies, prompt orchestration, and real-time data augmentation. The course emphasizes practical implementation using modern RAG architectures, covering ingestion pipelines, indexing strategies, query rewriting, reranking, and response synthesis. Participants will also learn evaluation techniques, latency optimization, security considerations, and cost-efficient deployment patterns used in enterprise AI applications. By the end of the program, learners will be able to design, build, and deploy scalable RAG solutions for use cases such as chatbots, enterprise search, knowledge assistants, and decision-support systems. This course is ideal for AI engineers, data scientists, and software professionals seeking to implement production-grade generative AI systems aligned with real-world business requirements.