Remove trailing spaces from Markdown

content/copilot/github-copilot-chat/about-github-copilot-chat.md

@@ -17,7 +17,7 @@ shortTitle: About Copilot Chat
 
 {% data variables.product.prodname_copilot_chat %} works by using a combination of natural language processing and machine learning to understand your question and provide you with an answer. This process can be broken down into a number of steps.
 
-### Input processing 
+### Input processing
 
 The input prompt from the user is pre-processed by the {% data variables.product.prodname_copilot_chat_short %} system and sent to a large language model to get a response based on the context and prompt. User input can take the form of code snippets or plain language. The system is only intended to respond to coding-related questions.
 
@@ -25,7 +25,7 @@ The input prompt from the user is pre-processed by the {% data variables.product
 
 The pre-processed prompt is then passed through the {% data variables.product.prodname_copilot_chat_short %} language model, which is a neural network that has been trained on a large body of text data. The language model analyzes the input prompt.
 
-### Response generation 
+### Response generation
 
 The language model generates a response based on its analysis of the input prompt. This response can take the form of generated code, code suggestions, or explanations of existing code.
 
@@ -41,19 +41,19 @@ The response generated by {% data variables.product.prodname_copilot_chat_short
 
 ### Generating unit test cases
 
-{% data variables.product.prodname_copilot_chat_short %} can help write unit test cases by generating code snippets based on the code open in the editor or the code snippet you highlight in the editor. This may help you write test cases without spending as much time on repetitive tasks. For example, if you are writing a test case for a specific function, you can use {% data variables.product.prodname_copilot_chat_short %} to suggest possible input parameters and expected output values based on the function's signature and body. {% data variables.product.prodname_copilot_chat_short %} can also suggest assertions that ensure the function is working correctly, based on the code's context and semantics. 
+{% data variables.product.prodname_copilot_chat_short %} can help write unit test cases by generating code snippets based on the code open in the editor or the code snippet you highlight in the editor. This may help you write test cases without spending as much time on repetitive tasks. For example, if you are writing a test case for a specific function, you can use {% data variables.product.prodname_copilot_chat_short %} to suggest possible input parameters and expected output values based on the function's signature and body. {% data variables.product.prodname_copilot_chat_short %} can also suggest assertions that ensure the function is working correctly, based on the code's context and semantics.
   
 {% data variables.product.prodname_copilot_chat_short %} can also help you write test cases for edge cases and boundary conditions that might be difficult to identify manually. For instance, {% data variables.product.prodname_copilot_chat_short %} can suggest test cases for error handling, null values, or unexpected input types, helping you ensure your code is robust and resilient. However, it is important to note that generated test cases may not cover all possible scenarios, and manual testing and code review are still necessary to ensure the quality of the code. For more information on generating unit test cases, see "[Asking {% data variables.product.prodname_copilot_chat %} questions about your code](/copilot/github-copilot-chat/using-github-copilot-chat#asking-github-copilot-chat-questions-about-your-code)."
 
 ### Explaining code
 
-{% data variables.product.prodname_copilot_chat_short %} can help explain selected code by generating natural language descriptions of the code's functionality and purpose. This can be useful if you want to understand the code's behavior or for non-technical stakeholders who need to understand how the code works. For example, if you select a function or code block in the code editor, {% data variables.product.prodname_copilot_chat_short %} can generate a natural language description of what the code does and how it fits into the overall system. This can include information such as the function's input and output parameters, its dependencies, and its purpose in the larger application. 
+{% data variables.product.prodname_copilot_chat_short %} can help explain selected code by generating natural language descriptions of the code's functionality and purpose. This can be useful if you want to understand the code's behavior or for non-technical stakeholders who need to understand how the code works. For example, if you select a function or code block in the code editor, {% data variables.product.prodname_copilot_chat_short %} can generate a natural language description of what the code does and how it fits into the overall system. This can include information such as the function's input and output parameters, its dependencies, and its purpose in the larger application.
 
 By generating explanations and suggesting related documentation, {% data variables.product.prodname_copilot_chat_short %} may help you to understand the selected code, leading to improved collaboration and more effective software development. However, it's important to note that the generated explanations and documentation may not always be accurate or complete, so you'll need to review, and occasionally correct, {% data variables.product.prodname_copilot_chat_short %}'s output.
 
 ### Proposing code fixes
 
-{% data variables.product.prodname_copilot_chat_short %} can propose a fix for bugs in your code by suggesting code snippets and solutions based on the context of the error or issue. This can be useful if you are struggling to identify the root cause of a bug or you need guidance on the best way to fix it. For example, if your code produces an error message or warning, {% data variables.product.prodname_copilot_chat_short %} can suggest possible fixes based on the error message, the code's syntax, and the surrounding code. 
+{% data variables.product.prodname_copilot_chat_short %} can propose a fix for bugs in your code by suggesting code snippets and solutions based on the context of the error or issue. This can be useful if you are struggling to identify the root cause of a bug or you need guidance on the best way to fix it. For example, if your code produces an error message or warning, {% data variables.product.prodname_copilot_chat_short %} can suggest possible fixes based on the error message, the code's syntax, and the surrounding code.
 
 {% data variables.product.prodname_copilot_chat_short %} can suggest changes to variables, control structures, or function calls that might resolve the issue and generate code snippets that can be incorporated into the codebase. However, it's important to note that the suggested fixes may not always be optimal or complete, so you'll need to review and test the suggestions.
 
@@ -105,7 +105,7 @@ Depending on factors such as your codebase and input data, you may experience di
 
 {% data variables.product.prodname_copilot_chat_short %} is capable of generating new code, which it does in a probabilistic way. While the probability that it may produce code that matches code in the training set is low, a {% data variables.product.prodname_copilot_chat_short %} suggestion may contain some code snippets that match code in the training set. {% data variables.product.prodname_copilot_chat_short %} utilizes filters that block matches with public code on {% data variables.product.prodname_dotcom %} repositories, but you should always take the same precautions as you would with any code you write that uses material you did not independently originate, including precautions to ensure its suitability. These include rigorous testing, IP scanning, and checking for security vulnerabilities. You should make sure your IDE or editor does not automatically compile or run generated code before you review it.
 
-### Inaccurate code 
+### Inaccurate code
 
 One of the limitations of {% data variables.product.prodname_copilot_chat_short %} is that it may generate code that appears to be valid but may not actually be semantically or syntactically correct or may not accurately reflect the intent of the developer. To mitigate the risk of inaccurate code, you should carefully review and test the generated code, particularly when dealing with critical or sensitive applications. You should also ensure that the generated code adheres to best practices and design patterns and fits within the overall architecture and style of the codebase.