small fixes

Author: misrasaurabh1

docs/docs/codeflash-concepts/how-codeflash-works.md

@@ -59,4 +59,4 @@ Codeflash implements several techniques to measure code performance accurately.
 
 ## Creating Pull Requests
 
-Once an optimization passes all checks, Codeflash creates a pull request through the Codeflash GitHub app directly in your repository. The pull request includes the new code, the speedup percentage, an explanation of the optimization, test statistics including coverage, and the test content itself. You can review and merge the new code if it meets your standar
+Once an optimization passes all checks, Codeflash creates a pull request through the Codeflash GitHub app directly in your repository. The pull request includes the new code, the speedup percentage, an explanation of the optimization, test statistics including coverage, and the test content itself. You can review, edit, and merge the new code.

docs/docs/intro.md

@@ -18,7 +18,7 @@ This offers high confidence that the behavior of your code remains unchanged.
 
 ### Continuous Optimization
 
-Because Codeflash is an automated process, you can install it as a GitHub action and have it optimize the new code on every pull request opened.
+Because Codeflash is an automated process, you can install it as a GitHub action and have it optimize the new code on every pull request.
 When Codeflash finds an optimization, it will ask you to review it. It will write a detailed explanation of the changes it made, and include all relevant info like % speed increase and proofs of correctness.
 
 This is a great way to ensure that your code, your team's code and your AI Agent's code are optimized for performance before it causes a performance regression. We call this *Continuous Optimization*.