diff --git a/website/docs/Getting-Started.md b/website/docs/Getting-Started.md
index a0d92c310..36ba35c52 100644
--- a/website/docs/Getting-Started.md
+++ b/website/docs/Getting-Started.md
@@ -11,9 +11,7 @@ AutoGen is a framework that enables development of LLM applications using multip
 * AutoGen enables building next-gen LLM applications based on multi-agent conversations with minimal effort. It simplifies the orchestration, automation and optimization of a complex LLM workflow. It maximizes the performance of LLM models and augments their weakness.
 * It supports diverse conversation patterns for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy,
 the number of agents, and agent conversation topology.
-* It provides a collection of working systems with different complexities. These systems
-span a wide range of applications from various domains and complexities. They demonstrate how
-AutoGen can easily support different conversation patterns.
+* It provides a collection of working systems with different complexities. These systems span a wide range of applications from various domains and complexities. They demonstrate how AutoGen can easily support different conversation patterns.
 
 AutoGen is powered by collaborative [research studies](/docs/Research) from Microsoft, Penn State University, and University of Washington.
 
@@ -22,8 +20,7 @@ AutoGen is powered by collaborative [research studies](/docs/Research) from Micr
 Install AutoGen from pip: `pip install pyautogen`. Find more options in [Installation](/docs/Installation).
 
 
-Autogen enables the next-gen GPT-X applications with a generic multi-agent conversation framework.
-It offers customizable and conversable agents which integrate LLMs, tools and human.
+Autogen enables the next-gen LLM applications with a generic multi-agent conversation framework. It offers customizable and conversable agents which integrate LLMs, tools and human.
 By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code. For example,
 ```python
 from autogen import AssistantAgent, UserProxyAgent
@@ -51,7 +48,8 @@ response = autogen.Completion.create(context=test_instance, **config)
 
 ### Where to Go Next?
 
-* Understand the use cases for [Autogen](/docs/Use-Cases/Autogen).
+* Understand the use cases for [multi-agent conversation](/docs/Use-Cases/multiagent_conversation).
+* Understand the use cases for [enhanced LLM inference](/docs/Use-Cases/enhanced_inference).
 * Find code examples from [Examples](/docs/Examples).
 * Learn about [research](/docs/Research) around AutoGen and check [blogposts](/blog).
 * Chat on [Discord](TBD).
diff --git a/website/docs/Research.md b/website/docs/Research.md
index 7806bbfae..ebcfffa01 100644
--- a/website/docs/Research.md
+++ b/website/docs/Research.md
@@ -1,6 +1,6 @@
 # Research
 
-For technical details, please check our research publications.
+For technical details, please check our technical report.
 
 * [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155) Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang and Chi Wang. ArXiv 2023.
 
diff --git a/website/docs/Use-Cases/multiagent_conversation.md b/website/docs/Use-Cases/multiagent_conversation.md
index 3373340d6..3c2c33b2b 100644
--- a/website/docs/Use-Cases/multiagent_conversation.md
+++ b/website/docs/Use-Cases/multiagent_conversation.md
@@ -1,6 +1,5 @@
 # Multi-agent conversation Framework
 
-
 AutoGen offers a unified multi-agent conversation framework as a high-level abstraction of using foundation models. It features capable, customizable and conversable agents which integrate LLM, tool and human via automated agent chat.
 By automating chat among multiple capable agents, one can easily make them collectively perform tasks autonomously or with human feedback, including tasks that require using tools via code.