Which LLMs can I access through this skill?

The skill supports models from various providers including Azure OpenAI (GPT), AWS Bedrock (Claude), Google Vertex AI (Gemini), Mistral, and Meta (Llama).

Does this skill support Retrieval-Augmented Generation (RAG)?

Yes, it provides patterns for grounding AI models with external business data from sources like SharePoint, S3, and SAP Build Work Zone.

What is the primary purpose of SAP AI Core?

SAP AI Core is a service on SAP BTP used to manage AI asset execution, model serving, and ML training in a scalable, hyperscaler-agnostic environment.

What service plans are required for Generative AI Hub?

Accessing the Generative AI Hub and LLMs requires the SAP AI Core Extended service plan on SAP BTP.

How does SAP AI Core handle data privacy?

It uses an orchestration service that includes PII data masking (anonymization and pseudonymization) and content filtering to ensure enterprise compliance.

SAP AI Core & Generative AI Hub

Name: SAP AI Core & Generative AI Hub
Author: secondsky

bysecondsky

•

Data Science & ML

Streamlines the deployment and management of enterprise AI/ML workloads and LLM orchestration on SAP BTP.

About

This skill provides production-ready guidance for developers working with SAP AI Core and AI Launchpad to build and scale enterprise AI applications. It enables seamless integration with the Generative AI Hub to access models from Azure OpenAI, AWS Bedrock, and Google Vertex AI, while implementing robust orchestration pipelines for data masking, content filtering, and Retrieval-Augmented Generation (RAG). Whether you are managing ML training pipelines via Argo Workflows or deploying secure LLM-powered applications, this skill offers the implementation patterns and API references needed for compliant, scalable AI development on SAP Business Technology Platform.

Key Features

Multi-provider LLM access via Generative AI Hub (GPT-4, Claude, Gemini, Llama)
Modular orchestration for templating, PII masking, and content filtering
Automated ML training and inference pipelines using Argo Workflows
2 GitHub stars
Enterprise-grade RAG implementation with vector database grounding
Comprehensive SAP AI Launchpad management and role configuration

Use Cases

Building secure, PII-compliant generative AI applications for SAP environments
Orchestrating multi-model AI workflows with advanced grounding and filtering
Scaling machine learning operations (MLOps) from training to production deployment on BTP

About

Key Features

Multi-provider LLM access via Generative AI Hub (GPT-4, Claude, Gemini, Llama)
Modular orchestration for templating, PII masking, and content filtering
Automated ML training and inference pipelines using Argo Workflows
2 GitHub stars
Enterprise-grade RAG implementation with vector database grounding
Comprehensive SAP AI Launchpad management and role configuration

Use Cases

Building secure, PII-compliant generative AI applications for SAP environments
Orchestrating multi-model AI workflows with advanced grounding and filtering
Scaling machine learning operations (MLOps) from training to production deployment on BTP