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AI Development Best Practices on SAP BTP

Comprehensive guide for implementing AI solutions on SAP Business Technology Platform using SAP AI Core and related services.

Source Repository: SAP-samples/sap-btp-ai-best-practices
Documentation Portal: https://btp-ai-bp.docs.sap/
Project Catalog: AI4U Project Catalog

Overview

SAP BTP provides AI capabilities through SAP AI Core, enabling both Generative AI (LLMs, chatbots, RAG) and Narrow AI (classical ML, predictions) implementations.

Requirements:

SAP Business Technology Platform account
Access to SAP AI Core service
Code examples available in: TypeScript, Python, Java, CAP

Generative AI Best Practices

1. Secure Access to AI Models

Access generative AI models through SAP AI Core with proper authentication:

# Python example - Secure model access
from gen_ai_hub.proxy.native.openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello, how can you help?"}]
)

// TypeScript example
import { OpenAI } from '@sap-ai-sdk/gen-ai-hub';

const client = new OpenAI();
const response = await client.chat.completions.create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: 'Hello, how can you help?' }]
});

Key Considerations:

Use SAP AI Core service keys for authentication
Configure environment variables from .env files
Never hardcode credentials in application code

2. Prompt Template Patterns

Create effective, reusable prompts:

# Prompt template pattern
SYSTEM_PROMPT = """You are a helpful assistant for {domain}.
Your task is to {task_description}.
Always respond in {language}."""

USER_PROMPT = """
Context: {context}
Question: {user_question}
"""

Best Practices:

Separate system and user prompts
Use placeholders for dynamic content
Include clear task descriptions
Specify output format expectations

3. Retrieval-Augmented Generation (RAG)

Implement RAG systems for grounding LLM responses with enterprise data:

Architecture:

Documents → Chunking → Embeddings → Vector Store
                                         ↓
User Query → Embedding → Similarity Search → Context
                                                ↓
                         LLM ← Context + Query → Response

Key Components:

Document chunking strategies
Vector embeddings (SAP AI Core embedding models)
Vector database (SAP HANA Cloud Vector Engine)
Context window management

4. Content Filtering

Implement content safety for AI-generated outputs:

Configure content filtering policies in SAP AI Core
Implement input validation before sending to models
Add output filtering for inappropriate content
Log and monitor filtered content for analysis

5. PII Data Masking

Protect personally identifiable information:

# Data masking pattern
def mask_pii(text: str) -> str:
    """Mask PII before sending to LLM"""
    # Replace emails, phone numbers, SSNs
    masked = re.sub(r'\b[\w.-]+@[\w.-]+\.\w+\b', '[EMAIL]', text)
    masked = re.sub(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b', '[PHONE]', masked)
    return masked

Best Practices:

Mask PII before sending to external models
Use SAP Data Privacy Integration where available
Implement reversible masking for response reconstruction
Audit PII handling in AI workflows

Narrow AI Best Practices

Regression Models

Classical ML for predictions (sales forecasting, demand planning):

Use SAP AI Core for model training and deployment
Implement proper feature engineering
Validate models with held-out test data
Monitor model drift in production

Anomaly Detection

Detect outliers in business data:

Use Cases:

Financial transaction monitoring
Quality control in manufacturing
Log analysis for system health
Document outlier detection

Approaches:

Statistical methods (z-score, IQR)
Machine learning (Isolation Forest, Autoencoders)
Time-series anomaly detection

AI Services Best Practices

SAP Document AI

Extract information from documents:

Invoice processing
Purchase order extraction
Contract analysis
Form recognition

SAP Translation Hub

Multilingual content translation:

Configure language pairs
Handle domain-specific terminology
Implement async translation for large documents

Use Cases Catalog

The repository includes 20+ end-to-end implementations:

Category	Use Case	Description
Chatbots & Agents	agentic-chatbot	Multi-tool AI agent implementation
	email-agent	Automated email processing and response
	post-sales-chatbot	Customer support automation
Document Processing	ai-pdf-information-extraction	Extract data from PDF documents
	diagram-to-bpmn	Convert diagrams to BPMN format
	sales-order-extractor	Extract sales order information
	rfqx-doc-analysis-utilities	RFQ document analysis
Procurement	intelligent-procurement-assistant	AI-powered procurement workflows
	intelligent-negotiation-assistant	Negotiation support with AI
	vendor-selection-optimization	Optimize vendor selection
Analytics	anomaly-detection	Detect anomalies in business data
	ai-log-analyzer	Analyze system logs with AI
	customer-credit-check	AI-assisted credit evaluation
	document-outlier-detection	Find outlier documents
Business Process	ai-powered-email-cockpit	Email classification and routing
	utilities-tariff-mapping-cockpit	Tariff mapping automation
	touchless-transactions-ai-agent	Automated GR/Invoice workflows
	product-catalog-search	AI-enhanced product search
	ai-capability-matcher	Match capabilities with AI

Quick Start

# Clone the repository
git clone [https://github.com/SAP-samples/sap-btp-ai-best-practices.git](https://github.com/SAP-samples/sap-btp-ai-best-practices.git)
cd sap-btp-ai-best-practices

# Navigate to a specific best practice
cd best-practices/generative-ai/access-to-ai-models/python
pip install -r requirements.txt
cp .env.example .env
# Edit .env with your SAP AI Core service key
python main.py

Resources

Resource	Link
Documentation Portal	https://btp-ai-bp.docs.sap/
GitHub Repository	https://github.com/SAP-samples/sap-btp-ai-best-practices
Project Catalog	https://ai4u-website.cfapps.eu10-004.hana.ondemand.com/project-catalog
SAP AI Core Documentation	https://help.sap.com/docs/sap-ai-core

License: Apache-2.0
Last Updated: 2025-11-22
Repository: https://github.com/secondsky/sap-skills

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