# 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](https://github.com/SAP-samples/sap-btp-ai-best-practices)  
**Documentation Portal**: [https://btp-ai-bp.docs.sap/](https://btp-ai-bp.docs.sap/)  
**Project Catalog**: [AI4U Project Catalog](https://ai4u-website.cfapps.eu10-004.hana.ondemand.com/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
# 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
// 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:

```python
# 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:

```python
# 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

```bash
# 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/](https://btp-ai-bp.docs.sap/) |
| **GitHub Repository** | [https://github.com/SAP-samples/sap-btp-ai-best-practices](https://github.com/SAP-samples/sap-btp-ai-best-practices) |
| **Project Catalog** | [https://ai4u-website.cfapps.eu10-004.hana.ondemand.com/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](https://help.sap.com/docs/sap-ai-core) |

---

**License**: Apache-2.0  
**Last Updated**: 2025-11-22  
**Repository**: [https://github.com/secondsky/sap-skills](https://github.com/secondsky/sap-skills)