fix typos, improves readability

README.md

@@ -83,7 +83,7 @@ The figure below shows an example conversation flow with AutoGen.
 
 Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples/AutoGen-AgentChat) for this feature.
 
-* Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` with powerful functionalities like tuning, caching, error handling, templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
+* Autogen also helps maximize the utility out of the expensive LLMs such as ChatGPT and GPT-4. It offers a drop-in replacement of `openai.Completion` or `openai.ChatCompletion` adding powerful functionalities like tuning, caching, error handling, and templating. For example, you can optimize generations by LLM with your own tuning data, success metrics and budgets.
 ```python
 # perform tuning
 config, analysis = autogen.Completion.tune(
@@ -126,7 +126,7 @@ a CLA and decorate the PR appropriately (e.g., status check, comment). Simply fo
 provided by the bot. You will only need to do this once across all repos using our CLA.
 
 This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
+For more information, see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
 contact [[email protected]](mailto:[email protected]) with any additional questions or comments.
 
 # Legal Notices

website/docs/Use-Cases/agent_chat.md

@@ -58,7 +58,7 @@ After the initialization step, the conversation could proceed automatically. Fin
 1. The assistant receives a message from the user_proxy, which contains the task description.
 2. The assistant then tries to write Python code to solve the task and sends the response to the user_proxy.
 3. Once the user_proxy receives a response from the assistant, it tries to reply by either soliciting human input or preparing an automatically generated reply. If no human input is provided, the user_proxy executes the code and uses the result as the auto-reply.
-4. The assistant sends another response to the user_proxy, who then decides whether to end the conversation or repeat steps 3 and 4.
+4. The assistant then generates a further response for the user_proxy. The user_proxy can then decide whether to terminate the conversation. If not, steps 3 and 4 are repeated.
 
 ### Supporting Diverse Conversation Patterns
 
@@ -69,7 +69,7 @@ On the one hand, one can achieve fully autonomous conversations after an initial
 
 By adopting the conversation-driven control with both programming language and natural language, AutoGen inherently allows dynamic conversation. Dynamic conversation allows the agent topology to change depending on the actual flow of conversation under different input problem instances, while the flow of a static conversation always follows a pre-defined topology. The dynamic conversation pattern is useful in complex applications where the patterns of interaction cannot be predetermined in advance. AutoGen provides two general approaches to achieving dynamic conversation:
 
-- Registered auto-reply. With the pluggable auto-reply function, one can choose to invoke conversations with other agents depending on the content of the current message and context. A working system demonstrating this type of dynamic conversation can be found in this code example, demonstrating a [dynamic group chat](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb). In the system, we register an auto-reply function in the group chat manager, which lets LLM decide who will the next speaker be in a group chat setting.
+- Registered auto-reply. With the pluggable auto-reply function, one can choose to invoke conversations with other agents depending on the content of the current message and context. A working system demonstrating this type of dynamic conversation can be found in this code example, demonstrating a [dynamic group chat](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb). In the system, we register an auto-reply function in the group chat manager, which lets LLM decide who the next speaker will be in a group chat setting.
 
 - LLM-based function call. In this approach, LLM decides whether or not to call a particular function depending on the conversation status in each inference call.
 By messaging additional agents in the called functions, the LLM can drive dynamic multi-agent conversation. A working system showcasing this type of dynamic conversation can be found in the [multi-user math problem solving scenario](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb), where a student assistant would automatically resort to an expert using function calls.