GenAI Data Engineering and RAG Systems
Build enterprise RAG pipelines, vector search, retrieval strategies, and knowledge systems
Learn how to turn enterprise documents into AI-ready data and build effective Retrieval-Augmented Generation systems. Explore the design of knowledge bases, optimize information retrieval, and deploy practical support applications to improve AI accuracy and relevance.
Starweaver | Jun 2026 | 173 min
What You Will Learn
You will start by exploring the foundations of GenAI architecture and RAG systems, then move into hands-on projects that cover data pipeline creation, retrieval workflows, and knowledge base design. Each section introduces practical techniques and real-world scenarios to help you build scalable, production-ready solutions.
Key Features
- Build and optimize AI-ready data pipelines for enterprise knowledge systems
- Design and implement RAG architectures using vector search and retrieval strategies
- Enhance retrieval quality with metadata filtering, reranking, and response validation
Target Audience
Ideal for data engineers, ML engineers, software engineers, and AI specialists ready to deepen their skills in knowledge-driven AI systems. If you have experience with data pipelines or enterprise data and want to implement advanced retrieval and RAG solutions, this course will help you reach your goals.
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