This course delivers a practical, hands-on journey through the foundations and real-world applications of Retrieval-Augmented Generation (RAG) for knowledge management and automation.
What you’ll learn:
- RAG search methods: How modern search and retrieval power context-aware AI systems
- Embedding models: Represent your data for relevant, accurate retrieval
- Working with VectorDBs: Store, index, and query data using leading vector databases
- Document chunking strategies: Break content into optimal units for retrieval and accuracy
- Building a complete RAG pipeline: Assemble all components into a production-ready workflow
Who should take this course:
- Developers and DevOps engineers responsible for documentation, compliance, or automation
- Platform, SRE, or cloud engineers looking to enhance knowledge workflows
- Anyone interested in deploying AI-driven search and knowledge-hub solutions No prior ML background required—just basic scripting and cloud fundamentals.
Outcomes:
- Build and tune high-performing RAG systems with real data and infrastructure
- Turn scattered documentation and policies into a trusted, easily searchable source of truth
- Automate knowledge retrieval and reduce manual effort across your stack
