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Course Outline
Day 1 – SAP GenAI Architecture & AI Landscape
Module 1 – Introduction to SAP Business AI
- SAP Business AI overview
- Generative AI use cases in SAP
- SAP AI strategy
- AI Foundation overview
- SAP AI Core
- SAP AI Launchpad
- SAP Joule overview
- SAP-ABAP-1 Foundation Model overview
Module 2 – SAP GenAI Architecture
- SAP BTP AI Services
- Generative AI Hub
- AI Core architecture
- AI orchestration
- Supported LLM providers
- AI deployment patterns
Module 3 – AI Developer Ecosystem
- DeepHub Copilot capabilities and positioning
- DeepHub Copilot vs SAP Joule vs GitHub Copilot
- Selecting the appropriate AI assistant for various development tasks
- Prompt engineering for enterprise development
- AI-assisted code review and documentation
- Responsible AI practices for developers
Hands-on Lab
- Exploring AI capabilities in SAP BTP
- Using AI assistants to explain and analyze existing ABAP code
Day 2 – ABAP AI SDK & Integration
Module 4 – Configuration & Enablement
- Entitlements
- Service Plans
- Destinations
- Trust Configuration
- Authentication
- Authorization concepts
- Connectivity between ABAP and AI services
Module 5 – ABAP AI SDK
- SDK architecture
- SDK APIs
- SDK Classes
- SDK best practices
- Error handling
Module 6 – Calling LLMs from ABAP
- Prompt construction
- Calling external LLMs
- Using SAP Generative AI Hub
- Response parsing
- Conversation context
- Token management
- Logging and monitoring
Hands-on Labs
- Creating your first AI-enabled ABAP application
- Building an ABAP application that invokes an LLM
- Generating summaries and business text from SAP data
- AI-assisted code generation and explanation using DeepHub Copilot
Day 3 – Advanced Development & Best Practices
Module 7 – GenAI in RAP
- RAP overview
- AI-enabled RAP Actions
- Business validations using AI
- AI-assisted business workflows
- Integrating AI into Fiori applications
Module 8 – Advanced AI Development
- Prompt engineering for enterprise scenarios
- Structured outputs
- AI orchestration
- Error handling
- Performance considerations
- Cost optimization
- Monitoring AI consumption
Module 9 – Governance & Security
- AI Governance
- Security architecture
- Data privacy
- Authorization
- Compliance
- Responsible AI
- AI auditability
Final Workshop – End-to-End Scenario
Participants will build an AI-enabled ABAP application that:
- Utilizes DeepHub Copilot during development for code generation, explanation, and refactoring.
- Invokes an LLM through SAP AI Core / Generative AI Hub.
- Implements AI-enabled RAP Actions.
- Applies security and governance best practices.
- Tests and monitors AI interactions.
Learning Outcomes
By the end of the course, participants will be able to:
- Understand SAP's Generative AI architecture.
- Configure SAP AI Core, AI Launchpad, and required services.
- Use the ABAP AI SDK to integrate LLMs into ABAP applications.
- Develop RAP applications enhanced with Generative AI.
- Leverage DeepHub Copilot effectively throughout the development lifecycle.
- Apply prompt engineering, governance, security, and cost optimization best practices in enterprise SAP AI solutions.
Requirements
Knowledge of:
- SAP ERP or SAP S/4HANA architecture
- Object-Oriented ABAP concepts
- REST APIs and HTTP communication
- Basic AI and Large Language Model (LLM) concepts (recommended)
Experience with:
- SAP ABAP development
- SAP Business Technology Platform (BTP) (recommended)
- Eclipse ADT (ABAP Development Tools)
- Developing reports, classes, and function modules
Programming Skills:
- Intermediate ABAP programming experience
- Familiarity with Object-Oriented Programming (OOP)
Audience
- SAP ABAP Developers
- SAP Technical Consultants
- SAP Solution Architects
- SAP Technical Leads
- SAP BTP Developers
- SAP Integration Specialists
- Enterprise Application Developers working with SAP
- AI/Innovation Engineers building SAP-based business applications
21 Hours