SAP AI Core Orchestration Service
The concept name was not provided (marked as "undefined"), and the supplied source snippets contain only tutorial titles without any descriptive body text or factual claims that could be used to construct an accurate, grounded definition. There is insufficient source material to write a valid 2–4 sentence prose definition for an unknown concept.
Tutorials that teach this
- Tutorial Implement a Custom ABAP AI Scenario Consuming SAP AI Core Orchestration Service in Your SAP S/4HANA System
- Tutorial Optimize Prompts for Tool Calling Using a Custom LLM-as-a-Judge Metric in SAP AI Core
- Tutorial 🔵 Devtoberfest 2025 - Week 3 - Data & AI sessions
- Tutorial Orchestration with Grounding Capabilities in SAP AI Core (Interview Question Generation Use Case)
- Tutorial Orchestration(V2) with Grounding Capabilities in SAP AI Core
- Tutorial Consumption of GenAI models Using Orchestration(V2) service - A Beginner's Guide
- Tutorial Custom Evaluation for Generative AI – Comprehensive Guide
- Tutorial Orchestration with Grounding Capabilities in SAP AI Core
- Tutorial Leveraging Orchestration Capabilities to Enhance Responses
- Tutorial GenAI Grounding Evaluations with SAP AI Core
Discovery missions teaching this
Prerequisites
- Concept Large Language Models (LLMs) The concept provided is **undefined**, and the supplied sources do not describe a specific, identifiable SAP developer concept by that name. Based on the available snippets — which cover topics such as [Retrieval Augmented Generation (RAG)](https://architecture.learning.sap.com/docs/ref-arch/RA0005/3-retrieval-augmented-generation/readme), prompting LLMs in the generative AI hub, and using custom or small language models on SAP AI Core — no definition can be responsibly constructed for an unnamed or undefined concept. Please provide a valid concept name so that an accurate, source-grounded definition can be written.
- Concept SAP BTP Service Instance and Service Key Creation The concept provided is `undefined`, and the supplied sources do not contain sufficient content to ground a meaningful definition of any specific SAP developer concept. Without a valid concept name or substantive source snippets, a documentation-quality definition cannot be written accurately or responsibly.
- Concept Generative AI Hub setup and onboarding The concept name provided is "undefined," and the supplied sources do not contain enough grounded information to produce a reliable, accurate definition for a specific SAP developer concept. Please provide a valid concept name and relevant source snippets so a precise definition can be written.
- Concept Retrieval Augmented Generation (RAG) The concept name was not provided (marked as "undefined"), and the available source snippets do not contain enough detail to identify or define a specific SAP developer concept with confidence. Based on the sources, the closest identifiable concept is **Retrieval Augmented Generation (RAG)**, which is a technique used to gain more control over the prompting results of a Large Language Model (LLM) by leveraging your own data. Developers use it within [Generative AI on SAP BTP](https://architecture.learning.sap.com/docs/ref-arch/RA0005/readme) to ground LLM responses in enterprise-specific content, for example through [HANA vector search or SAP AI Core orchestration](https://architecture.learning.sap.com/docs/ref-arch/RA0005/3-retrieval-augmented-generation/readme).
- Concept SAP Cloud SDK for Java The concept name was not provided (marked as "undefined"), and the supplied source snippets consist only of tutorial titles without any descriptive content or factual claims to draw from. There is insufficient information in the provided sources to write a grounded, accurate reference definition.
- Concept SAP BTP Service Instance and Binding Creation The provided sources do not contain sufficient information to write a grounded definition for an **undefined** concept. Source [S1] references a troubleshooting scenario involving the SAP Service Manager binding on SAP BTP, while the remaining sources ([S2]–[S8]) are tutorial titles only, with no descriptive content. Without a clearly identified concept name and supporting source content, a factually grounded definition cannot be produced.
- Concept SAP AI Core Model Deployment and Inference The concept name provided is "undefined," and the supplied sources do not contain a definition or description of a specific, identifiable SAP developer concept that can be documented. Without a valid concept name or sufficient source content to ground a definition, a accurate and source-supported definition cannot be produced.
- Concept SAP BTP Subaccount and Entitlement Setup The provided sources do not contain sufficient information to define this concept. The concept name is "undefined" and none of the source snippets supply grounded content that could be used to write an accurate, sourced definition.
- Concept Prompt Template Engineering The concept name was not provided (received "undefined"), so a meaningful definition cannot be written. Please supply a valid SAP developer concept name so that a grounded, accurate definition can be produced from the available sources.
- Concept SAP BTP Fundamentals and Platform Navigation
- Concept SAP AI Core Service Setup The concept name was not provided, so a precise definition cannot be constructed from the available sources. Based on the sources, these materials collectively cover building and integrating [Generative AI applications](https://architecture.learning.sap.com/docs/golden-path/ai-golden-path/2-build-and-deliver/3-genai-applications/readme) on SAP Business Technology Platform using SAP AI Core — a managed service that developers use to train models, run AI workloads, and connect to large language models via orchestration. Developers leverage it alongside [SAP BTP service instances and keys](https://help.sap.com/docs/btp/sap-business-technology-platform/5b35ee98403045309bb0f8c7f0c79365?locale=en-US&state=PRODUCTION&version=Cloud) to authenticate and consume AI capabilities such as prompt optimization, grounding, multimodal responses, and custom evaluation within their applications.
- Concept Generative AI Hub & LLM Integration via SAP AI Core The concept name was not provided (received "undefined"), so a grounded definition cannot be written. Please supply a valid SAP developer concept name so that a definition can be crafted from the available sources.
- Concept Generative AI Foundation Models
- Concept SAP Generative AI Hub The concept name was not provided, so a best-effort definition is derived from the available sources covering SAP's AI development landscape. [SAP's AI Golden Path](https://architecture.learning.sap.com/docs/golden-path/ai-golden-path/readme) is a curated starting point for developing AI applications across the SAP ecosystem, offering recommendations and guidance for building and delivering AI-powered solutions. Developers use it to navigate capabilities such as [Generative AI application integration, embeddings and semantic search](https://architecture.learning.sap.com/docs/ref-arch/RA0005/2-semantic-search/readme), predictive and tabular AI, and document processing. It encompasses patterns for leveraging Foundation Models and Large Language Models (LLMs) through prompting, as well as frameworks like Intelligent Scenario Lifecycle Management (ISLM) for standardized AI lifecycle management.
Concepts that build on this
- Concept Intelligent Scenario Lifecycle Management
- Concept LLM orchestration across multiple models
- Concept Prompt Registry Management in SAP Generative AI Hub
- Concept Intelligent Scenario for Generative AI
- Concept PII Data Masking for AI Applications
- Concept ISLM Intelligent Scenario Management
- Concept AI Content Filtering and Data Masking in Orchestration
- Concept Generative AI Custom Evaluation in SAP AI Core
- Concept Devtoberfest Data & AI Sessions Overview
- Concept AI-Driven Machine Translation
- Concept Document Grounding via Microsoft SharePoint in SAP AI Core