Concept card

Grounding & RAG (Retrieval-Augmented Generation)

As of 2026-07-24T14:00:00Z

Edited by Cédric-Olivier Mâry, sole editor and publication director — 27 years in the SAP ecosystem. How these pages are produced, and where automation is used.

What is Grounding & RAG (Retrieval-Augmented Generation)?

Without RAG grounding, LLMs hallucinate confidently on data that may be 18 months stale — with it, every Joule answer traces to a citation chain that answers the auditor's 'where did this come from?' in 30 seconds.

What it is

Retrieval-Augmented Generation, RAG, is the architectural pattern that grounds a large language model's answers in retrieved enterprise data instead of relying solely on what the model happened to memorise during training. The flow is straightforward to describe even though the engineering underneath is not: a user asks a question, a retrieval layer fetches relevant documents or records from a corpus, that retrieved context is placed alongside the user's prompt, the model generates a response constrained by what it was just shown, and citations link the answer back to the source records it drew from. For SAP analytics, RAG is the foundation underneath Joule's grounding capability and the real differentiator against "ChatGPT pointed at your files" alternatives that skip the governance layer.

The five components

Indexing builds the corpus: source documents and records are embedded into a vector store using semantic embeddings. Retrieval happens at query time — the user's prompt is embedded and the top-K most similar chunks are fetched, typically somewhere between three and ten. Re-ranking is an optional second stage that uses a cross-encoder model to refine that top-K list before anything reaches the language model. Generation is where the model receives the prompt, the retrieved context, and system instructions, and produces its response. Citation closes the loop by attaching pointers back to the specific documents or records the response actually drew on.

Why it works in SAP analytics

Why it matters

Structured retrieval against Datasphere Analytic Models, document retrieval, and Knowledge Graph traversal are three distinct RAG sources most production Joule deployments combine, not a single mechanism.
The citation chain is what turns an LLM answer into an auditable artifact — without it, a plausible-sounding answer and a correct one are indistinguishable to the business user.
Re-ranking (an optional cross-encoder stage) refines the top-K retrieved chunks — skipping it on high-stakes queries risks feeding the LLM near-miss context.

Key points

Five components: index · retrieve · re-rank · generate · cite.
Three SAP sources: Datasphere AM · documents · Knowledge Graph.
Chunk size 500-1500 tokens; Top-K 5-10 with re-ranking.
Citations mandatory — defeats grounding purpose if absent.
EEA-resident LLM for EU personal data + SCC + TIA per Schrems-II.
Per-query cost $0.01-0.05; tenant ~$7.5k/year typical.
Re-index on source-system release + nightly delta.
Knowledge Graph + RAG fusion signalled 12-24 mo.
Grounding & RAG (Retrieval-Augmented Generation) is mastered only when it changes a named buyer decision.
Start with the semantic contract and control model before demonstrating the tool.

Terms used on this page

RAG (Retrieval-Augmented Generation): LLM pattern grounding responses in retrieved enterprise data instead of training-data recall.
Vector store: Database of semantic embeddings indexing the corpus. Allows similarity-based retrieval.
Embedding: Numeric vector representation of text (or other content) capturing semantic meaning.
Top-K retrieval: Retrieving the K most-similar chunks at query time. Sweet spot 5-10 with re-ranking.
Cross-encoder re-ranking: Optional second-stage scoring refining top-K. High ROI on top-3 quality.
Citation chain: Auditable pointer trail from answer back to source documents/records consumed.
Hybrid retrieval: Combining BM25 (lexical) + dense vector (semantic) for broader recall.
Agentic-RAG: Multi-hop retrieval with LLM-driven reasoning between retrievals. Preview pattern.

Sources

Full card available to members. What the full card adds: the full decision framework · the SAP vs Snowflake / Databricks / Fabric comparison · the common pitfalls and their fix · the cheat sheet · the architecture schemas · the code blocks · the facts worth quoting.

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