diff --git a/benchmarking.qmd b/benchmarking.qmd
index fdd54d8b7..e3687f66c 100644
--- a/benchmarking.qmd
+++ b/benchmarking.qmd
@@ -2,14 +2,14 @@
 
 ## Introduction
 
-Explanation: Introducing the concept and importance of benchmarking sets the stage for the reader to understand why it is crucial in the evaluation and optimization of AI systems, especially in resource-constrained embedded environments.
+Explanation: Introducing the concept and importance of benchmarking sets the stage for the reader to understand why it is crucial in the evaluation and optimization of AI systems, especially in resource-constrained embedded environments where it is even more important! 
 
 - Importance of benchmarking in AI
 - Objectives of benchmarking
 
 ## Types of Benchmarks
 
-Explanation: Understanding the different types of benchmarks helps readers tailor their benchmarking activities to specific needs, whether they are evaluating low-level operations or entire application performance.
+Explanation: Understanding the different types of benchmarks will help our readers tailor their performance evaluation activities to specific needs, whether they are evaluating low-level operations or entire application performance.
 
 - Micro-benchmarks
 - Macro-benchmarks
@@ -24,6 +24,7 @@ Explanation: Metrics are the yardsticks used to measure performance. This sectio
 - Throughput
 - Power Consumption
 - Memory Footprint
+- End to end Metrics (User vs. System)
 
 ## Benchmarking Tools
 
@@ -34,13 +35,13 @@ Explanation: Tools are the practical means to carry out benchmarking. Discussing
 
 ## Benchmarking Process
 
-Explanation: Outlining the step-by-step process of benchmarking provides a structured approach for readers, ensuring that they can conduct benchmarks in a systematic and repeatable manner.
+Explanation: Outlining the step-by-step process of benchmarking provides a structured approach for readers, ensuring that they can conduct benchmarks in a systematic and repeatable manner.e
 
-- Data Preparation
+- Dataset Limitation/Sources
 - Model Selection
 - Test Environment Setup
 - Running the Benchmarks
-- Data Collection
+- Run Rules
 
 ## Interpreting Results