update title

Author: HomelessDinosaur

docs/guides/python/llama-rag.mdx

@@ -1,5 +1,5 @@
 ---
-description: 'Transform your LLMs with Retrieval Augmented Generation'
+description: 'Making LLMs smarter with Dynamic Knowledge Access using Retrieval Augmented Generation'
 tags:
   - API
   - AI & Machine Learning
@@ -12,7 +12,7 @@ published_at: 2024-11-15
 updated_at: 2024-11-15
 ---
 
-# Using Retrieval Augmented Generation to enhance your LLMs
+# Making LLMs smarter with Dynamic Knowledge Access
 
 This guide shows how to use Retrieval Augmented Generation (RAG) to enhance a large language model (LLM). RAG is the process of enabling an LLM to reference context outside of its initial training data before generating its response. It can be extremely expensive in both time and computing power to train a model that is useful for your own domain-specific purposes. Therefore, using RAG is a cost-effective alternative to extending the capabilities of an existing LLM.