LLM Batch Inference (Asynchronous)
The concept name was not provided, and the supplied source snippet contains only a tutorial title ("Batch Consumption") without any descriptive content to ground a definition. A meaningful, source-grounded definition cannot be written without either a valid concept name or substantive source text.
Tutorials that teach 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 JSON Lines (JSONL) File Format for Batch Requests The concept name was not provided, and the single source snippet supplies only a tutorial title ("Batch Consumption") without any descriptive content to ground a definition. A accurate, source-grounded definition cannot be written without either a valid concept name or substantive source material.
- Concept SAP AI Core Object Store Secret Configuration The concept name provided is undefined and no valid concept was supplied for definition. Additionally, the provided source snippets contain only tutorial titles without any substantive content to ground a definition. Please provide a valid SAP developer concept name and relevant source content so an accurate definition can be written.
- 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 Machine Learning Inference **Intelligent Scenario Lifecycle Management (ISLM)** is an SAP BTP capability that enables developers and administrators to manage the full lifecycle of intelligent scenarios — including machine learning models and AI-based predictions — within SAP applications. According to the [official documentation](https://help.sap.com/docs/btp/sap-business-technology-platform/2217809cdb5842bf98211c61a8ef55ca?locale=en-US&state=PRODUCTION&version=Cloud), it provides tooling to govern how intelligent scenarios are deployed, activated, and maintained. Developers use it to operationalize AI scenarios such as sales order completion, service ticket classification, and recommendation models within the SAP ecosystem.
- 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.