Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) is a technique in which an AI model first retrieves relevant information from a defined knowledge source and then uses that information to generate its answer.
AI
Explanation
A RAG process typically works in three steps:
1. Indexing: documents – such as policies, configuration guides, or project documentation – are prepared and stored so they can be searched by meaning.
2. Retrieval: for each question, the most relevant passages are found.
3. Generation: the passages are added to the prompt, and the language model formulates an answer based on them, ideally with references to the sources.
RAG allows a model to use current and company-specific information without retraining it.
Why It Matters
Accounting policies, SAP configuration, and project decisions are specific to each company. RAG helps AI answer questions based on these sources rather than general training knowledge, which reduces hallucinations and makes answers traceable. The quality of the result depends on the quality, structure, and currency of the underlying documents.
Related Terms
Large Language Model · Hallucination · Model Context Protocol · Prompt Engineering
Related Solution:
Ready to Make Your SAP Finance Landscape Agentic?
Whether you are implementing SAP RAR, modernizing revenue processes, or exploring agentic finance operations, Revident helps you move from strategy to execution.