Update README.md

README.md

@@ -9,9 +9,8 @@ tags:
 - llama-index
 ---
 # Summary:
-    Retrieval Augmented Generation (RAG) is a technique to specialize a language model with a specific knowledge domain by feeding in relevant data so that it can give better answers.
-# Implemeting RAG(in a nutshell):
-### 1. Ready/ Preprocess your input data:
-    Language Models see all the data as tokens and vectors. So we want to convert the data to be fed into the same format.
-### 2. Feed the processed data to the Language Model.
-### 3. Indexing the stored data that matches the context of the query.
+  Retrieval Augmented Generation (RAG) is a technique to specialize a language model with a specific knowledge domain by feeding in relevant data so that it can give better answers.
+# How does RAG works?
+    1. Ready/ Preprocess your input data i.e. tokenization & vectorization
+    2. Feed the processed data to the Language Model.
+    3. Indexing the stored data that matches the context of the query.