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by Eddiesecondsky/sap-skills456 stars
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Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models, building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, managing prompt templates via the Prompt Registry, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM, Perplexity), orchestration modules, embeddings, tool calling, and structured outputs.

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referencesai-launchpad-guide.md

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SAP AI Launchpad Complete Guide

Comprehensive reference for SAP AI Launchpad features and operations.

Documentation Source: https://github.com/SAP-docs/sap-artificial-intelligence/tree/main/docs/sap-ai-launchpad

Table of Contents

  1. Overview
  2. Initial Setup
  3. Workspaces and Connections
  4. User Roles
  5. Generative AI Hub
  6. Prompt Editor
  7. Prompt Registry
  8. Prompt Optimization
  9. Orchestration Workflows
  10. ML Operations
  11. Configurations
  12. Deployments
  13. Executions and Runs
  14. Schedules
  15. Datasets and Artifacts
  16. Model Comparison
  17. Applications
  18. Meta API and Custom Runtime Capabilities

Overview

SAP AI Launchpad is a multitenant SaaS application on SAP BTP that provides:

  • Management UI for AI runtimes (SAP AI Core)
  • Generative AI Hub for prompt experimentation
  • ML Operations for model lifecycle management
  • Analytics and monitoring dashboards

Two User Types

Type Description
AI Scenario Producer Engineers developing and productizing AI scenarios
AI Scenario Consumer Business analysts subscribing to and using AI scenarios

Initial Setup

Prerequisites

  1. SAP BTP enterprise account
  2. Subaccount with Cloud Foundry enabled
  3. SAP AI Launchpad subscription
  4. SAP AI Core instance (for runtime connection)

Setup Steps

  1. Create Subaccount with Cloud Foundry environment
  2. Subscribe to SAP AI Launchpad in Service Marketplace
  3. Create Service Instance of SAP AI Core (if needed)
  4. Assign Role Collections to users
  5. Add Connection to SAP AI Core runtime

Service Plans

Plan Cost Support GenAI Hub
Free Free Community only, no SLA No
Standard Monthly fixed price Full SAP support Yes

Note: Free → Standard upgrade preserves data; downgrade not supported.

Workspaces and Connections

Adding a Connection

  1. Navigate to Administration → Connections
  2. Click Add
  3. Enter connection details:
    • Name
    • Service Key (from SAP AI Core)
  4. Test connection
  5. Save

Managing Connections

Operation Description
Edit Modify connection settings
Delete Remove connection
Test Verify connectivity
Set Default Make primary connection

Assigning Connection to Workspace

  1. Navigate to Workspaces
  2. Select workspace
  3. Click Assign Connection
  4. Select connection from dropdown
  5. Confirm

User Roles

Administrative Roles

Role Capabilities
ailaunchpad_admin Full administrative access
ailaunchpad_connections_editor Manage connections
ailaunchpad_aicore_admin SAP AI Core integration management

ML Operations Roles

Role Capabilities
ailaunchpad_mloperations_viewer View ML operations
ailaunchpad_mloperations_editor Full ML operations access

Generative AI Hub Roles

Role Capabilities
genai_manager Full GenAI hub access, save prompts
genai_experimenter Prompt experimentation only
prompt_manager Manage saved prompts
prompt_experimenter Use saved prompts

Functions Explorer Roles

Role Capabilities
ailaunchpad_functions_explorer_editor_v2 Edit functions explorer
ailaunchpad_functions_explorer_viewer_v2 View functions explorer

Note: Role names prompt_media_executor and orchestration_executor may be deprecated. Verify current role names in SAP documentation.

Generative AI Hub

Access Path

Workspaces → Select workspace → Generative AI Hub

Features

Feature Description
Prompt Editor Interactive prompt testing
Model Library Browse available models
Grounding Management Manage document pipelines
Orchestration Build workflow configurations
Chat Direct model interaction
Saved Prompts Prompt management

Model Library

View model specifications including:

  • Capabilities (chat, embeddings, vision)
  • Context window sizes
  • Performance benchmarks
  • Cost per token
  • Deprecation dates

Prompt Editor

Access

Generative AI Hub → Prompt Editor

Interface Elements

Element Description
Name Prompt identifier (manager roles only)
Collection Organize prompts (manager roles only)
Messages Configure message blocks with roles
Variables Define input placeholders
Model Selection Choose model and version
Parameters Adjust model parameters
Metadata Tags and notes (manager roles only)

Message Roles

  • System: Instructions for the model
  • User: User input
  • Assistant: Previous assistant responses

Variable Syntax

Use {{variable_name}} for placeholders with definitions section.

Running Prompts

  1. Configure messages and variables
  2. Select model (optional - uses default)
  3. Adjust parameters
  4. Click Run
  5. View response (streaming available)

Image Inputs

  • Supported for select models (GPT-4o, Gemini, Llama Vision)
  • Maximum 5MB across all inputs
  • Requires prompt_media_executor role

Saving Prompts

  • Click Save (manager roles only)
  • Assign to collection
  • Add tags and notes
  • Version automatically managed

Prompt Types

Type Description
Question Answering Q&A interactions
Summarization Extract key points
Inferencing Sentiment, entity extraction
Transformations Translation, format conversion
Expansions Content generation

Prompt Registry

Access: Generative AI Hub → Prompt Registry

The Prompt Registry manages the lifecycle of prompt templates from design to runtime. It integrates prompt templates into SAP AI Core, making them discoverable across applications and orchestration workflows.

Two Management Interfaces

Interface Method Best For Versioning
Imperative API REST API (full CRUD) Design-time refinement History tracked via /history endpoint
Declarative API Git repository sync Runtime use cases, CI/CD Git-managed, auto-synced

Imperative API: Create a Prompt Template

Send a POST request to {{apiurl}}/v2/lm/promptTemplates:

{
  "name": "summarization-template",
  "scenarioId": "foundation-models",
  "version": "0.0.1",
  "template": [
    {"role": "system", "content": "Summarize the following text concisely."},
    {"role": "user", "content": "{{?input_text}}"}
  ],
  "defaults": {
    "input_text": ""
  },
  "additional_fields": {}
}

Declarative API: Create via Git

  1. Create a prompt template file: <name>.prompttemplate.ai.sap.yaml
  2. Push to a synced git repository
  3. The template auto-syncs within minutes
  4. Verify via GET {{apiurl}}/v2/lm/promptTemplates
  5. Declarative templates are marked managedBy: <declarative> and are always the head version

Using a Prompt Template at Runtime

Fill a template by ID:

POST {{apiurl}}/v2/lm/promptTemplates/{{promptTemplateId}}/substitution

Or fill by name, scenario, and version:

POST {{apiurl}}/v2/lm/scenarios/{{scenarioId}}/promptTemplates/{{promptTemplateName}}/versions/{{versionId}}/substitution

Include variable values in the request body. Add query parameter metadata=true to return the full template definition.

Resource Group Scope

By default, prompt templates are managed at the main-tenant level. To scope to a resource group:

--header 'AI-Resource-Group-Scope: true'
--header 'AI-Resource-Group: <resource group>'

Integration with Orchestration

Prompt templates from the registry can be referenced in orchestration workflows via the templating module, enabling centralized prompt management across multiple applications.

Required Roles

Role Capabilities
prompt_manager Create, update, delete prompt templates
prompt_experimenter Use saved prompt templates

Prompt Optimization

Access: Generative AI Hub → Prompt Optimization

Prompt Optimization evaluates and refines prompts using reference datasets. Available since Q1 2026.

Key Features

  • Variable mapping to reconcile mismatches between prompts and datasets
  • Metrics including EXACT_MATCH (Boolean: output exactly matches reference)
  • Automated evaluation against ground-truth responses

Variable Mapping

When prompt variable names differ from dataset column names, variable mapping aligns them:

{
  "variable_mapping": [
    {"prompt_variable": "user_query", "dataset_column": "question"},
    {"prompt_variable": "context_text", "dataset_column": "document"}
  ]
}

Configuration

Create a configuration for prompt optimization via the API, referencing a prompt template from the Prompt Registry and a dataset in the object store.

Orchestration Workflows

Access

Generative AI Hub → Orchestration → Create

Workflow Modules

Order Module Required
1 Grounding Optional
2 Templating Mandatory
3 Input Translation Optional
4 Data Masking Optional
5 Input Filtering Optional
6 Model Configuration Mandatory
7 Output Filtering Optional
8 Output Translation Optional

Required Modules Explained:

  • Templating: Constructs the actual prompt/messages sent to the LLM using input variables and context
  • Model Configuration: Specifies which LLM model to use and its parameters (temperature, max_tokens, etc.)

Building Workflows

  1. Click Create to start new workflow
  2. Configure required modules (Templating, Model)
  3. Enable optional modules via Edit
  4. Configure each enabled module
  5. Click Test to run workflow
  6. Click Save to store configuration

JSON Upload

  • Maximum file size: 200 KB
  • Format: JSON with module_configurations
  • Note: Workflows with images can be downloaded but not uploaded

Saving Workflows

  • Save as configuration for reuse
  • Assign name and description
  • Link to deployments

ML Operations

Access

Workspaces → Select workspace → ML Operations

Components

Component Purpose
Configurations Parameter and artifact settings
Executions Training jobs
Deployments Model serving
Schedules Automated executions
Datasets Training data
Models Trained models
Result Sets Inference outputs
Other Artifacts Miscellaneous artifacts

Configurations

Creating Configuration

  1. Navigate to ML Operations → Configurations
  2. Click Create
  3. Enter details:
    • Name
    • Scenario
    • Executable
    • Parameters
    • Input artifacts
  4. Save

Configuration Contents

Field Description
Name Configuration identifier
Scenario AI scenario reference
Executable Workflow or serving template
Parameter Bindings Key-value parameters
Artifact Bindings Input artifact references

Deployments

Creating Deployment

  1. Navigate to ML Operations → Deployments
  2. Click Create
  3. Select configuration
  4. Set duration (optional TTL)
  5. Click Create

Deployment Details

Field Description
ID Unique identifier
Status Current state
URL Inference endpoint
Configuration Associated config
Created Timestamp
Duration TTL if set

Deployment Statuses

Status Description Actions
Pending Starting Stop
Running Active Stop
Stopping Shutting down Wait
Stopped Inactive Delete
Dead Failed Delete
Unknown Initial Delete

Operations

Operation Description
View See deployment details
View Logs Access pipeline logs
Update Change configuration
Stop Halt deployment
Delete Remove deployment

Bulk Operations

  • Stop multiple deployments
  • Delete multiple deployments (up to 100)

Executions and Runs

Creating Execution

  1. Navigate to ML Operations → Executions
  2. Click Create
  3. Select configuration
  4. Click Create

Execution Statuses

Status Description
Pending Queued
Running Executing
Completed Finished successfully
Dead Failed
Stopped Manually stopped

Viewing Execution Details

  • Parameters and artifacts
  • Status and timing
  • Logs from pipeline
  • Output artifacts
  • Metrics

Comparing Executions

  1. Select multiple executions
  2. Click Compare
  3. View side-by-side:
    • Parameters
    • Metrics
    • Durations
  4. Create charts for visualization

Schedules

Creating Schedule

  1. Navigate to ML Operations → Schedules
  2. Click Create
  3. Select configuration
  4. Set cron expression
  5. Define start/end dates
  6. Save

Cron Expression Format

┌───────── minute (0-59)
│ ┌─────── hour (0-23)
│ │ ┌───── day of month (1-31)
│ │ │ ┌─── month (1-12)
│ │ │ │ ┌─ day of week (0-6)
│ │ │ │ │
* * * * *

Schedule Operations

Operation Description
View See schedule details
Edit Modify schedule
Stop Pause schedule
Resume Restart schedule
Delete Remove schedule

Datasets and Artifacts

Dataset Registration

  1. Navigate to ML Operations → Datasets
  2. Click Register
  3. Enter details:
    • Name
    • URL (ai://secret-name/path)
    • Scenario
    • Description
  4. Save

Artifact Types

Type Description
Dataset Training/validation data
Model Trained model
Result Set Inference results
Other Miscellaneous

Finding Artifacts

  • Filter by scenario
  • Search by name
  • Sort by date
  • View details

Model Comparison

Comparing Models

  1. Navigate to ML Operations → Models
  2. Select multiple models
  3. Click Compare
  4. View:
    • Configuration differences
    • Metric comparisons
    • Performance charts

Creating Comparison Charts

  1. Select metrics to compare
  2. Choose chart type
  3. Configure axes
  4. Generate visualization

Applications

Managing Applications

Access: Administration → Applications

Operations

Operation Description
Create Add new application
View See application details
Edit Modify settings
Delete Remove application
Create Disclaimer Add usage disclaimer

Chat Application

Create chat interfaces using deployed models:

  1. Create application
  2. Configure model deployment
  3. Set disclaimer (optional)
  4. Share application URL

Meta API and Custom Runtime Capabilities

The Meta API identifies which capabilities apply to a given AI runtime, allowing SAP AI Launchpad to display only relevant features.

Purpose

Function Description
Capability Management Enable/disable capabilities based on AI use case
UI Streamlining Hide unnecessary features to reduce confusion
API Decoupling Reduce impact of backend API changes

Supported Capabilities

Capability Description
userDeployments Allows users to create custom deployments
userExecutions Enables execution functionality
staticDeployments System-managed deployments
timeToLiveDeployments TTL-based deployment limits
bulkUpdates Bulk operations support
executionSchedules Scheduling functionality
analytics Analytics dashboard

Metadata Refresh

  • Automatic: Refreshed periodically on schedule
  • On-demand: Users can trigger manual refresh
  • Administration: SAP Runtime team manages active capabilities

Custom Runtime Usage

Custom runtimes can selectively implement only necessary capabilities, creating a tailored experience:

AI Runtime → Meta API Query → Capability List → Filtered UI

Accessibility Features

SAP AI Launchpad provides:

  • Keyboard navigation
  • Screen reader support
  • High contrast themes
  • Accessible UI components

Language Settings

Change interface language:

  1. Navigate to user settings
  2. Select language preference
  3. Save changes

Supported languages vary by region and deployment.

Documentation Links

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub16d

    This skill provides a comprehensive documentation suite and template library for developing with SAP AI Core and SAP AI Launchpad on the SAP Business Technology Platform (BTP). It includes detailed guidance on model orchestration, RAG (Retrieval-Augmented Generation), and ML training pipelines. The skill emphasizes security best practices, including content filtering and data masking, and no malicious patterns were identified.

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Signed by skilld at 620a19a. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 weeks ago.

Activeupdated 2 months ago

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Other metadata
metadata
{
  "maintainer": "Eduard Jiglau",
  "maintainer_email": "hello@sap-ai-skills.com",
  "website": "https://sap-ai-skills.com",
  "version": "2.4.1",
  "last_verified": "2026-06-12",
  "production_tested": "No; documentation-audited only, no live tenant/runtime evidence",
  "runtime_verification": "pending tenant evidence"
}

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