Learning outcomes

  • This course will prepare you to:
    • Describe how large language models work, including key capabilities, limitations, and enterprise considerations
    • Identify the SAP's AI Services and Models and explain the role of the Generative AI Hub within it
    • Navigate the Generative AI Hub interface — Model Library, Chat, Prompt Editor, Prompt Optimizer, Prompt Management, Grounding Management, and Orchestration
    • Identify the Orchestration Service and explain how it serves as the deployment and workflow layer for LLMs in SAP
    • Access the Generative AI Hub by provisioning SAP AI Core, configuring resource groups, and deploying LLMs
    • Develop and iterate enterprise prompts using the Prompt Editor, including JSON-structured output for application integration
    • Manage prompts at scale using the Prompt Registry — templates, versioning, variables, and prompt hardening
    • Apply prompt engineering techniques — one-shot, few-shot, meta prompting, and system/user/assistant roles
    • Define key security considerations in prompt engineering and apply prompt hardening methods
    • Design AI workflows using the Orchestration Service, combining prompt templates, data masking, and content filtering
    • Use the SAP Cloud SDK for AI to connect applications programmatically to the Generative AI Hub and Orchestration Services
    • Explain retrieval-augmented generation and implement document grounding using the SAP HANA Vector Engine, including multi-repository patterns
    • Use the Prompt Optimizer to automatically test and refine prompts against defined quality criteria
    • Evaluate and select large language models using benchmark data, cost, and context window criteria from the Model Library
    • Apply SAP AI Core Custom Evaluation to benchmark LLMs against custom test datasets and scoring criteria
    • Identify SAP's own foundation models — SAP-RPT-1 and SAP-ABAP-1 — and determine when to use tabular AI instead of generative language models

Content

  • Describing Large Language Models
  • Discovering SAP's Generative AI Hub
  • Discovering the Orchestration Service
  • Getting Started: Access, Deployment, and the Product Lifecycle
  • Developing and Managing Prompts
  • Refining Prompts with Engineering Techniques
  • Designing Workflows with the Orchestration Service
  • Leveraging SAP Cloud SDK for AI
  • Improving LLM Performance
  • Using Advanced AI Techniques — Grounding
  • Evaluating and Selecting Large Language Models

Audience

  • Data Analyst
  • Developer
  • Data Scientist

Course Based on Software Release

  • SAP AI Core
  • SAP AI Launchpad 

Languages

Available in English

Prerequisites

Essential

  • None

Recommended

  • Basic programming experience (Python or similar)
  • Familiarity with SAP Business Technology Platform concepts

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Languages

Available in English

Prerequisites

Essential

  • None

Recommended

  • Basic programming experience (Python or similar)
  • Familiarity with SAP Business Technology Platform concepts