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Can generative AI actually answer questions using your company’s private SAP policies, documents, and business knowledge? What happens when an AI model needs the right enterprise context before generating an answer? This is where Retrieval-Augmented Generation (RAG), embeddings, and vector search become important. In this video, we break down how RAG works with a practical SAP procurement example and explain where SAP HANA Cloud Vector capabilities fit into the architecture. Whether you are an SAP Functional Consultant, ABAP/BTP Developer, Data Consultant, Architect, or technical professional, understanding this pattern can help you see how enterprise AI solutions are built.

Chapters:
00:00 - Introduction: The Core Challenge of AI in Enterprise Knowledge
00:26 - Understanding Retrieval-Augmented Generation (RAG): "Retrieve First, Generate Second"
01:18 - Vector Search & Embeddings: Multi-Dimensional Semantic Meaning
01:53 - SAP HANA Cloud Vector Capabilities & Document Ingestion Pipelines
02:40 - Step-by-Step Architecture Walkthrough: Policy Search to Grounded AI Answer
03:44 - Roles & Responsibilities: Functional Consultants vs. Technical Developers
04:53 - ZaranTech SAP AI Masterclass Overview & Conclusion

In this video, you’ll learn:
What RAG (Retrieval-Augmented Generation) means and why enterprises need it
How the Retrieve → Augment → Generate pattern works
Why AI should retrieve relevant enterprise information instead of using every company document
What embeddings are and how they represent the semantic meaning of content
How vector search finds information based on meaning rather than only keywords
Where SAP HANA Cloud Vector capabilities fit into an enterprise RAG architecture
Why documents must be extracted, chunked, and converted into embeddings before vector search
How a practical SAP procurement-policy question moves through a complete RAG architecture
Why SAP Functional Consultants need to understand business context, source documents, validation, and access requirements
What ABAP, BTP, Data, and Integration Consultants need to understand about building production RAG solutions
Why RAG is an enterprise architecture pattern—not simply connecting an LLM to SAP

Think of the complete flow as: Enterprise Documents → Extraction → Chunking → Embeddings → HANA Vector Search → Retrieved Context → LLM → Grounded Response. The future of SAP AI is not just about knowing what an LLM is. It is about giving AI the right business context, at the right time, with the right controls.

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Assessments
Lab Documents
Certification Q & A
Course Completion Certificate
E-Books
Certification Guides
Configuration Docs

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