SAP HANA Vector Engine & GenAI Online Training Certification Course

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Accelerate enterprise AI innovation with SAP HANA Vector Engine & GenAI Training by Multisoft Systems. Gain practical expertise in vector embeddings, similarity search, semantic data retrieval, RAG workflows, and LLM integration while learning to connect SAP business data with intelligent, context-aware Generative AI applications.

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SAP HANA Vector Engine & GenAI Online Training Certification Course Course Overview

SAP HANA Vector Engine & GenAI Training by Multisoft Systems is a specialized technical program designed for professionals seeking to use enterprise data with modern Generative AI applications. The course explains how SAP HANA Cloud can store, process, and search vector embeddings alongside structured business data, enabling semantic search and AI-driven information retrieval within enterprise environments.

Participants explore vector concepts, embedding generation, vector data handling, similarity measurement, semantic search, and Retrieval-Augmented Generation (RAG). The training further examines how retrieved enterprise context can be supplied to Large Language Models (LLMs) to generate more relevant and context-aware responses. Learners also gain exposure to SAP BTP and GenAI integration patterns, application connectivity, APIs, security, governance, and performance considerations.

The course emphasizes practical implementation scenarios in which SAP business information, documents, product information, knowledge bases, and other enterprise datasets can be transformed into searchable vector representations and incorporated into GenAI applications.

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SAP HANA Vector Engine & GenAI Online Training Certification Course Course curriculum

Curriculum Designed by Experts

SAP HANA Vector Engine & GenAI Training by Multisoft Systems is a specialized technical program designed for professionals seeking to use enterprise data with modern Generative AI applications. The course explains how SAP HANA Cloud can store, process, and search vector embeddings alongside structured business data, enabling semantic search and AI-driven information retrieval within enterprise environments.

Participants explore vector concepts, embedding generation, vector data handling, similarity measurement, semantic search, and Retrieval-Augmented Generation (RAG). The training further examines how retrieved enterprise context can be supplied to Large Language Models (LLMs) to generate more relevant and context-aware responses. Learners also gain exposure to SAP BTP and GenAI integration patterns, application connectivity, APIs, security, governance, and performance considerations.

The course emphasizes practical implementation scenarios in which SAP business information, documents, product information, knowledge bases, and other enterprise datasets can be transformed into searchable vector representations and incorporated into GenAI applications.

  • Understand vector processing capabilities within SAP HANA Cloud.
  • Explain embeddings and their role in semantic AI applications.
  • Store and query vectorized enterprise information in SAP HANA.
  • Perform vector similarity and semantic searches.
  • Prepare and transform enterprise content for embedding generation.
  • Design retrieval pipelines for Generative AI applications.
  • Understand and implement RAG architecture concepts.
  • Integrate SAP HANA-based retrieval with LLMs and GenAI services.
  • Design enterprise semantic-search and knowledge-assistant solutions.
  • Apply security, governance, performance, and production considerations to GenAI implementations.

Course Prerequisite

  • Basic understanding of SAP HANA or relational databases
  • Familiarity with SQL and database concepts
  • Basic programming or application-development knowledge
  • General understanding of APIs and application integration
  • Fundamental awareness of cloud technologies
  • Basic knowledge of AI, machine learning, or Generative AI concepts is beneficial but not mandatory

Course Target Audience

  • SAP HANA Developers
  • SAP HANA Consultants
  • SAP BTP Developers and Consultants
  • SAP Technical Consultants
  • Generative AI Developers
  • AI/ML Engineers
  • Data Engineers
  • Data Architects
  • Solution Architects
  • Application Developers
  • Integration Developers
  • Technical Leads
  • Professionals working on enterprise AI and RAG implementations

Course Content

  • SAP HANA Cloud architecture overview
  • Role of SAP HANA in modern data architectures
  • Introduction to Generative AI
  • Large Language Models and enterprise applications
  • Structured versus unstructured enterprise data
  • Limitations of traditional keyword-based retrieval
  • Role of semantic search in GenAI
  • SAP ecosystem for enterprise AI
  • Position of SAP HANA Vector Engine in GenAI architecture

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  • Introduction to SAP HANA Cloud Vector Engine
  • Vector processing concepts
  • Vector Engine components and capabilities
  • Vector processing within SAP HANA Cloud
  • Combining relational and vector data
  • Vector workloads and enterprise data
  • Vector Engine architecture patterns
  • Data flow across GenAI applications
  • Understanding vector-based retrieval architecture

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  • Understanding vectors and multidimensional representations
  • What are embeddings?
  • Text-to-vector transformation
  • Semantic representation of enterprise content
  • Embedding dimensions
  • Embedding spaces
  • Similarity between embeddings
  • Dense vector concepts
  • Documents, chunks, and embeddings
  • Embedding model selection considerations
  • Embedding lifecycle in GenAI applications

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  • Vector data representation in SAP HANA
  • REAL_VECTOR data type
  • Creating tables for vector data
  • Storing embeddings with business data
  • Loading vector information
  • Converting vector representations
  • Vector-related SQL operations
  • Managing vector columns
  • Combining vector and conventional attributes
  • Querying structured and vector information
  • Managing document metadata with embeddings

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  • Fundamentals of similarity search
  • Semantic similarity concepts
  • Query vectors
  • Distance and similarity measurements
  • Cosine similarity concepts
  • Euclidean distance concepts
  • Dot-product-based similarity
  • Vector distance functions
  • Ranking retrieved results
  • Top-K retrieval
  • Threshold-based retrieval
  • Metadata-assisted filtering
  • Hybrid retrieval patterns
  • Evaluating retrieval relevance

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  • Role of embedding models
  • Embedding generation workflow
  • Query and document embeddings
  • Choosing appropriate embedding models
  • Understanding LLM context
  • Prompt and context relationships
  • Connecting enterprise information with LLMs
  • Model endpoint considerations
  • Embedding consistency
  • Handling model changes and re-embedding
  • Token and context-window considerations
  • Enterprise LLM integration patterns

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  • Traditional search versus semantic search
  • Semantic retrieval workflow
  • Preparing enterprise documents
  • Document extraction and preprocessing
  • Chunking strategies
  • Generating embeddings for chunks
  • Storing chunks and metadata
  • Creating semantic queries
  • Executing vector similarity searches
  • Ranking relevant passages
  • Combining semantic search with business filters
  • Improving retrieval quality
  • Semantic enterprise knowledge search scenario

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  • Introduction to RAG
  • Why RAG is used with LLMs
  • RAG architecture components
  • Ingestion pipeline
  • Document preprocessing
  • Chunking and embedding pipeline
  • Vector storage layer
  • User-query embedding
  • Retrieval workflow
  • Context augmentation
  • Prompt construction
  • LLM response generation
  • Grounding responses with enterprise data
  • Reducing hallucination risks
  • RAG versus model fine-tuning
  • RAG architecture with SAP HANA Vector Engine

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  • Designing an SAP HANA-based RAG pipeline
  • Preparing enterprise knowledge sources
  • Data ingestion strategies
  • Document chunk management
  • Embedding generation
  • Persisting embeddings in SAP HANA
  • Performing vector retrieval
  • Applying metadata filters
  • Selecting relevant context
  • Prompt enrichment
  • Passing retrieved context to an LLM
  • Processing generated responses
  • Source and context traceability
  • Retrieval quality evaluation
  • End-to-end RAG application workflow

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  • SAP AI architecture overview
  • Introduction to SAP AI Core
  • Role of SAP AI Launchpad
  • Introduction to Generative AI Hub
  • Model access through Generative AI Hub
  • Foundation model concepts
  • Model selection considerations
  • Connecting GenAI models with SAP applications
  • SAP HANA Vector Engine as a retrieval layer
  • Embedding and inference workflows
  • Prompt orchestration concepts
  • Combining HANA data with Generative AI Hub models
  • Enterprise GenAI integration architecture

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  • Designing enterprise GenAI applications
  • Application architecture considerations
  • Backend integration with SAP HANA Cloud
  • Connecting applications to vector search
  • Query processing workflow
  • Context retrieval and augmentation
  • LLM request and response handling
  • API-based integration concepts
  • Integration with SAP BTP applications
  • CAP-based application integration concepts
  • Conversational application patterns
  • Knowledge assistants and enterprise copilots
  • Handling sessions and conversational context
  • Error handling and fallback strategies

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  • Enterprise AI security considerations
  • SAP HANA authentication and authorization concepts
  • Protecting sensitive enterprise information
  • Role-based data access
  • Securing model and application connections
  • Data privacy considerations
  • Responsible AI principles
  • Prompt and response governance
  • Grounding and information traceability
  • Vector storage considerations
  • Retrieval performance optimization
  • Managing large vector datasets
  • Query optimization considerations
  • Monitoring GenAI applications
  • Cost and resource considerations
  • Production deployment considerations

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Enterprise Use Cases

  • Intelligent enterprise knowledge search
  • Product information assistant
  • Customer support knowledge assistant
  • HR policy and employee information assistant
  • Technical documentation search
  • Procurement and supplier information retrieval
  • Financial document intelligence
  • SAP business data question-answering scenarios
  • Context-aware enterprise copilots

Capstone Project – SAP HANA Vector Engine RAG Application

  • Define enterprise GenAI use case
  • Prepare sample knowledge documents
  • Design document chunking strategy
  • Generate vector embeddings
  • Store vectors and metadata in SAP HANA
  • Implement similarity-based retrieval
  • Apply business and metadata filters
  • Retrieve relevant contextual information
  • Construct augmented prompts
  • Integrate an LLM
  • Generate context-grounded responses
  • Test retrieval accuracy
  • Evaluate generated responses
  • Review security and governance considerations
  • Present complete GenAI solution architecture

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SAP HANA Vector Engine & GenAI Training (MCQ) Assessment

This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :

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SAP HANA Vector Engine & GenAI Online Training Certification Course Trainer Profile

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Our SAP HANA Vector Engine & GenAI Corporate & Certification Program trainers bring 13+ years of proven industry expertise, delivering practical insights aligned with real project environments.

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SAP HANA Vector Engine & GenAI Online Training Certification Course FAQ's

It is a technical training program focused on using SAP HANA's vector capabilities for embeddings, similarity search, semantic retrieval, RAG, and enterprise Generative AI applications.

Vector embeddings are numerical representations of content that allow applications to identify semantic relationships and retrieve information based on meaning rather than relying solely on exact keyword matches.

Yes. The curriculum includes vector storage, vector operations, similarity queries, semantic retrieval, embedding workflows, and their application in GenAI scenarios.

Yes. RAG is a major part of the program, covering architecture, data preparation, embedding generation, vector retrieval, context construction, LLM interaction, and response grounding.

Yes. Participants learn the architectural and application concepts involved in combining retrieved SAP HANA data with Large Language Models for context-aware response generation.

Yes. The course discusses SAP BTP integration patterns relevant to SAP HANA Cloud and enterprise GenAI applications, including GenAI service integration concepts.

The program is suitable for SAP HANA professionals, SAP BTP consultants, developers, AI/ML engineers, data engineers, architects, and professionals involved in enterprise GenAI projects.

Prior GenAI experience is useful but not essential. However, familiarity with databases, SQL, application development, or SAP HANA will help participants understand the technical concepts more effectively.

Yes. The curriculum concludes with a capstone scenario involving data preparation, embeddings, SAP HANA vector storage, semantic retrieval, RAG orchestration, LLM integration, and solution evaluation.

To contact Multisoft Systems, you can email us at info@multisoftsystems.com or call for a course enquiry at +91 9810306956

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