diff --git a/doc/RAG.md b/doc/RAG.md
index ae17f88..f4dfae6 100644
--- a/doc/RAG.md
+++ b/doc/RAG.md
@@ -10,7 +10,7 @@ By integrating RAG capabilities directly into Business Process Management (BPM)
 
 ## 2. Introduction & Problem Statement
 
-Retrieval-Augmented Generation (RAG) represents a significant advancement in AI-powered information systems, combining the generative capabilities of Large Language Models (LLMs) with dynamic information retrieval from external knowledge bases. Unlike traditional LLMs that rely solely on their training data, RAG systems
+Retrieval-Augmented Generation (RAG) represents a significant advancement in AI-powered information systems, combining the generative capabilities of Large Language Models (LLMs) with dynamic information retrieval from internal knowledge bases. Unlike traditional LLMs that rely solely on their training data, RAG systems
 
 1. first search through current document repositories to **retrieve** relevant information,
 2. then use this retrieved context to **argument** the problem