diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -16,9 +16,9 @@ Data recipes have two types: (i) Exact memories, eg '*What is the population of
 
 Given the rapidly changing landscape of LLMs, we have tried as much as possible to implement data recipes in such a way that it can be intergrated with various semantic architectures and frameworks. By implementing recipes using a recipes server (powered by [Robocorps actions server](https://github.com/robocorp/robocorp#readme)), it can be called from [Open AI assistant](https://platform.openai.com/docs/assistants/overview) actions and [Copilot Studio](https://www.microsoft.com/en-us/microsoft-copilot/microsoft-copilot-studio) as well from any custom code. Also included in this repo is an example of using recipes via OpenAI format plugins, as supported by frameworks such as [semantic kernel](https://learn.microsoft.com/en-us/semantic-kernel/overview/?tabs=Csharp). 
 
-Data recipes supports datasources accessed via API, but in some cases it is preferable to ingest data in order to leverage LLM SQL capabilities. We include an initial set of data sources specific to Humanitarian Response in the ingestion module, which can be extended to include additional sources as required.
+Data recipes supports datasources accessed via API, but in some cases it is preferable to ingest data in order to leverage LLM SQL capabilities. We include an initial set of data sources specific to humanitarian response in the ingestion module, which can be extended to include additional sources as required.
 
-Finally, for reviewing/updating/creating new recipes, though we provide some experimental assistants that can generate and run recipes to semi-automate the process, in talking with developers and datascientists, most would prefer to use their existing environment for development, such as VS Code + GitHub Copilot. For this reason we are not developing a dedicated user interface for this, and intead provide a sync process that will allow recipe managers to check out and work on recipes locally, then publish them back into the recipes database for wider consumption. We do include however a autogen studio setup to be able to use agent teams to create recipes.
+Finally, we provide a sync process that will allow recipe managers to check out and work on recipes locally, then publish them back into the recipes database for wider consumption. We also provide a autogen studio setup enabling the recipe managers to use experimental assistants to generate and run recipes, semi-automating the process. We are not developing a dedicated user interface because most developers and data scientists would prefer to use their existing development environment (such as VS Code + GitHub Copilot) for reviewing, updating, and creating new recipes.
 
 Some more discussion on design decisions can also be found [here](https://www.loom.com/share/500e960fd91c44c282076be4b0126461?sid=83af2d6c-622c-4bda-b21b-8f528d6eafba).
 
@@ -169,4 +169,4 @@ You will need to set key environment variables, see your local `.env` for exampl
 
 ## Databases
 
-When running in Azure it is useful to use remote databases, at least for the mongodb instance so that user logins are retained with each release. For example, a databse can be configured by following [these instructions](https://docs.librechat.ai/install/configuration/mongodb.html). If doing this, then docker-compose-azure.yml in Azure can have the mongo DB section removed, and any instance of the Mongo URL used by other containers updated with the cloud connection string accordingly.
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+When running in Azure it is useful to use remote databases, at least for the mongodb instance so that user logins are retained with each release. For example, a databse can be configured by following [these instructions](https://docs.librechat.ai/install/configuration/mongodb.html). If doing this, then docker-compose-azure.yml in Azure can have the mongo DB section removed, and any instance of the Mongo URL used by other containers updated with the cloud connection string accordingly.