Update recipes/use_cases/end2end-recipes/RAFT-Chatbot/README.md

Author: HamidShojanazeri

recipes/use_cases/end2end-recipes/RAFT-Chatbot/README.md

@@ -4,7 +4,7 @@ As the popularity of our Meta Llama 3 models grows, we've seen a surge in demand
 
 In response to this demand, we're exploring the possibility of building a Llama chatbot that can answer Llama-related questions using our Meta Llama 3 models. In this tutorial, we'll demonstrate how to do just that. While our Meta Llama 3 70B Instruct model is an excellent candidate, its production costs are relatively high. To reduce these costs, we'll focus on creating a Llama chatbot based on the Meta Llama 8B Instruct model, aiming to achieve similar accuracy while minimizing inference costs.
 
-One common ML approach to produce a model based on new domain data is **fine-tuning**. The idea is to start from a pre-trained model that already has some knowledge of language from its pre-training and adapt it to a new domain. However, [recent paper](https://arxiv.org/pdf/2405.05904) highlights the risk of using supervised fine-tuning to update LLMs' knowledge, as it presents empirical evidence that acquiring new knowledge through fine-tuning is correlated with hallucinations w.r.t. preexisting knowledge. Fine-tuning can also be costly if the domain knowledge has to be updated frequently.
+One common approach to produce a model based on new domain data is **fine-tuning**. The idea is to start from a pre-trained model that already has some knowledge of language from its pre-training and adapt it to a new domain. However, [recent paper](https://arxiv.org/pdf/2405.05904) highlights the risk of using supervised fine-tuning to update LLMs' knowledge, as it presents empirical evidence that acquiring new knowledge through fine-tuning is correlated with hallucinations w.r.t. preexisting knowledge. Fine-tuning can also be costly if the domain knowledge has to be updated frequently.
 
 Another solution is to use **RAG (Retrieval-Augmented Generation)**, which combines the strengths of traditional information retrieval systems (such as databases) with the capabilities of generative large language models (LLMs). RAG operates by first retrieving relevant information from a database using a query generated by the LLM. This retrieved information is then integrated into the LLM's query input, enabling it to generate more accurate and contextually relevant text. This helps to reduce LLM hallucination as the related documents are provided to LLM and has a lower cost to update the domain knowledge.