diff --git a/docs/source/cli.rst b/docs/source/cli.rst
index a3cc2b47d1215..07a37ae821011 100644
--- a/docs/source/cli.rst
+++ b/docs/source/cli.rst
@@ -4,7 +4,7 @@ Command-Line Interface
 ======================
 
 The MLflow command-line interface (CLI) provides a simple interface to various functionality in MLflow. You can use the CLI to run projects, start the tracking UI, create and list experiments, download run artifacts,
-serve MLflow Python Function and scikit-learn models, serve MLflow Python Function and scikit-learn models, and serve models on
+serve MLflow Python Function and scikit-learn models, and serve models on
 `Microsoft Azure Machine Learning <https://azure.microsoft.com/en-us/services/machine-learning-service/>`_
 and `Amazon SageMaker <https://aws.amazon.com/sagemaker/>`_.
 
diff --git a/docs/source/llm-tracking.rst b/docs/source/llm-tracking.rst
index 1bb122feff1e1..ca3a1d006fca7 100644
--- a/docs/source/llm-tracking.rst
+++ b/docs/source/llm-tracking.rst
@@ -6,7 +6,7 @@ MLflow LLM Tracking
 
 The Mlflow LLM Tracking component consists of two elements for logging and viewing the behavior of LLM's.
 Firstly it is a set of APIs that allow for logging inputs, outputs, and prompts submitted and returned
-from LLM's. Accompanying these APIs is a UI components that provides a simplified means of viewing the
+from LLM's. Accompanying these APIs is a UI component that provides a simplified means of viewing the
 results of experimental submissions (prompts and inputs) and the results (LLM outputs).
 
 .. contents:: Table of Contents