LLMs on Enterprise Data

What you will learn

• Architect a production-grade RAG pipeline on SAP enterprise data — covering chunking strategy for BW/4HANA InfoProviders and Datasphere views, domain-adapted embedding model selection via SAP AI Core, and hybrid dense-plus-sparse retrieval using HANA Cloud's native vector engine.
• Diagnose why SAP Joule responses degrade in low-metadata environments and implement the semantic layer enrichment programme — field descriptions, measure documentation, dimension labelling — that grounds Joule's retrieval context in business meaning.
• Design LLM output validation controls for SAP analytics use cases: SQL execution validation for natural-language-to-BW-MDX applications, fact-checking against retrieved data for narrative generation, and prompt-level guard rails that enforce non-hallucination constraints.
• Apply enterprise governance to LLM deployments on SAP data: design prompt audit logs, retrieval provenance trails, GDPR-compliant access control mirroring from the underlying SAP system to the RAG retrieval corpus, and output flagging mechanisms that meet regulated-industry requirements.

The Grounding Problem: Why Enterprise LLMs Fail Without Architecture

Deploying a large language model against enterprise data is not a product decision — it is an architecture decision with significant downstream consequences for accuracy, cost, governance, and security. The default failure mode is vivid and well-documented: an LLM asked about a company's SAP BW data answers confidently and incorrectly, blending its training knowledge with hallucinated specifics about the client's data model. The answer sounds authoritative. It is wrong.

The solution space is not "add a disclaimer." It is a Retrieval-Augmented Generation (RAG) architecture with explicit grounding, combined with governance controls that treat LLM outputs as a data product subject to the same quality standards as a SAC dashboard. This module teaches the mechanics of that architecture applied to SAP enterprise data environments.

SAP Joule — SAP's generative AI copilot embedded across S/4HANA, SuccessFactors, Ariba, and the SAP Analytics Cloud — is the most visible example of an enterprise LLM deployment on SAP data. Understanding what Joule does architecturally, where it succeeds, where it fails, and what you must build around it to make it production-grade is the practitioner's starting point.

How SAP Joule Actually Works: RAG Over SAP Metadata

Prerequisites

• Intermediate hands-on experience on SAP analytics projects
• Review core concepts first: C035, C027, C087

Outcomes

• Explain why Joule response quality is capped by semantic-layer metadata quality, not by model choice, and prioritise metadata enrichment before pipeline investment.
• Design a hybrid dense-plus-sparse retrieval pipeline on HANA Cloud, or an external vector store once past its exact-search ceiling, with the choice driven by corpus size and access-control governance needs.
• Specify an execute-and-validate gate for LLM-generated SQL/MDX and a fact-check gate for narrative outputs before either reaches a business decision.
• Apply this grounding-and-governance pattern to a live Joule or custom-RAG pilot and produce a go/no-go memo defensible to a risk/control stakeholder.