edit pass: prompt-flow-batch1

Author: paulth1

articles/machine-learning/prompt-flow/tools-reference/embedding-tool.md

@@ -1,7 +1,7 @@
 ---
 title: Embedding tool in Azure Machine Learning prompt flow
 titleSuffix: Azure Machine Learning
-description: The prompt flow embedding tool uses OpenAI's embedding models to convert text into dense vector representations for various NLP tasks.
+description: The prompt flow embedding tool uses OpenAI's embedding models to convert text into dense vector representations for various natural language processing tasks.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: prompt-flow
@@ -16,7 +16,7 @@ ms.date: 11/02/2023
 
 # Embedding tool
 
-OpenAI's embedding models convert text into dense vector representations for various NLP tasks. For more information, see the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings).
+OpenAI's embedding models convert text into dense vector representations for various natural language processing tasks. For more information, see the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings).
 
 ## Prerequisites
 

articles/machine-learning/prompt-flow/tools-reference/llm-tool.md

@@ -1,7 +1,7 @@
 ---
 title: LLM tool in Azure Machine Learning prompt flow
 titleSuffix: Azure Machine Learning
-description: The prompt flow large language model (LLM) tool enables you to take advantage of widely used LLMs like OpenAI or Azure OpenAI for natural language processing.
+description: The prompt flow LLM tool enables you to take advantage of widely used large language models like OpenAI or Azure OpenAI for natural language processing.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: prompt-flow
@@ -16,9 +16,9 @@ ms.date: 11/02/2023
 
 # LLM tool
 
-The prompt flow large language model (LLM) tool enables you to take advantage of widely used LLMs like [OpenAI](https://platform.openai.com/) or [Azure OpenAI Service](../../../cognitive-services/openai/overview.md) for natural language processing.
+The prompt flow large language model (LLM) tool enables you to take advantage of widely used large language models like [OpenAI](https://platform.openai.com/) or [Azure OpenAI Service](../../../cognitive-services/openai/overview.md) for natural language processing.
 
-Prompt flow provides a few different LLM APIs:
+Prompt flow provides a few different large language model APIs:
 
 - [Completion](https://platform.openai.com/docs/api-reference/completions): OpenAI's completion models generate text based on provided prompts.
 - [Chat](https://platform.openai.com/docs/api-reference/chat): OpenAI's chat models facilitate interactive conversations with text-based inputs and responses.
@@ -94,5 +94,5 @@ The following sections show various inputs.
 ## Use the LLM tool
 
 1. Set up and select the connections to OpenAI resources.
-1. Configure the LLM model API and its parameters.
+1. Configure the large language model API and its parameters.
 1. Prepare the prompt with [guidance](prompt-tool.md#write-a-prompt).

articles/machine-learning/prompt-flow/tools-reference/overview.md

@@ -24,7 +24,7 @@ The following table provides an index of tools in prompt flow. If existing tools
 | [LLM](./llm-tool.md) | Uses Open AI's large language model (LLM) for text completion or chat. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Prompt](./prompt-tool.md) | Crafts a prompt by using Jinja as the templating language. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Embedding](./embedding-tool.md) | Uses Open AI's embedding model to create an embedding vector that represents the input text. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
-| [Open Source LLM](./open-source-llm-tool.md) | Uses an open-source model from the Azure Model catalog, deployed to an Azure Machine Learning online endpoint for LLM Chat or Completion API calls. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
+| [Open Source LLM](./open-source-llm-tool.md) | Uses an open-source model from the Azure Model catalog, deployed to an Azure Machine Learning online endpoint for large language model Chat or Completion API calls. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Serp API](./serp-api-tool.md) | Uses Serp API to obtain search results from a specific search engine. | Default | [promptflow-tools](https://pypi.org/project/promptflow-tools/) |
 | [Content Safety (Text)](./content-safety-text-tool.md) | Uses Azure Content Safety to detect harmful content. | Default | [promptflow-tools](https://pypi.org/project/promptflow-contentsafety/) |
 | [Faiss Index Lookup](./faiss-index-lookup-tool.md) | Searches a vector-based query from the Faiss index file. | Default | [promptflow-vectordb](https://pypi.org/project/promptflow-vectordb/) |

articles/machine-learning/prompt-flow/tools-reference/prompt-tool.md

@@ -16,7 +16,7 @@ ms.date: 11/02/2023
 
 # Prompt tool
 
-The prompt tool in prompt flow offers a collection of textual templates that serve as a starting point for creating prompts. These templates, based on the Jinja2 template engine, facilitate the definition of prompts. The tool proves useful when prompt tuning is required prior to feeding the prompts into the large language model (LLM) in prompt flow.
+The prompt tool in prompt flow offers a collection of textual templates that serve as a starting point for creating prompts. These templates, based on the Jinja2 template engine, facilitate the definition of prompts. The tool proves useful when prompt tuning is required prior to feeding the prompts into the large language model in prompt flow.
 
 ## Inputs
 

articles/machine-learning/prompt-flow/tools-reference/python-tool.md

@@ -110,7 +110,7 @@ If you're developing a Python tool that requires calling external services with
 
 ### Create a custom connection
 
-Create a custom connection that stores all your large language model (LLM) API key or other required credentials.
+Create a custom connection that stores all your large language model API key or other required credentials.
 
 1. Go to prompt flow in your workspace, and then go to the **Connections** tab.
 1. Select **Create** > **Custom**.