Update 3-executorch-workflow.md

Fixes

Author: madeline-underwood

content/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/3-executorch-workflow.md

@@ -7,32 +7,31 @@ weight: 3
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+##  How the ExecuTorch workflow operates
 
 Before setting up your environment, it helps to understand how ExecuTorch processes a model and runs it on Arm-based hardware.
 
-##  How the ExecuTorch workflow operates
-
-ExecuTorch works in three main stages:
+ExecuTorch works in three main steps:
 
 **Step 1: Export the model**
 
-   - Convert a trained PyTorch model into an operator graph.
-   - Identify operators that can be offloaded to the Ethos-U NPU (for example, ReLU, conv, quantize).
+   - Convert a trained PyTorch model into an operator graph
+   - Identify operators that can be offloaded to the Ethos-U NPU (for example, ReLU, conv, and quantize)
 
 **Step 2: Compile with the AOT compiler**
 
-   - Translate the operator graph into an optimized, quantized format.
-   - Use `--delegate` to move eligible operations to the Ethos-U accelerator.
-   - Save the compiled output as a `.pte` file.
+   - Translate the operator graph into an optimized, quantized format
+   - Use `--delegate` to move eligible operations to the Ethos-U accelerator
+   - Save the compiled output as a `.pte` file
 
 **Step 3: Deploy and run**
 
-   - Execute the compiled model on an FVP or physical target.
-   - The Ethos-U NPU runs delegated operators; all others run on the Cortex-M CPU.
+   - Execute the compiled model on an FVP or physical target
+   - The Ethos-U NPU runs delegated operators - all others run on the Cortex-M CPU
 
 ## Visual overview
 
-![Diagram showing the three-stage ExecuTorch workflow from model export to deployment#center](./how-executorch-works-high-level.png)
+![Diagram showing the three-step ExecuTorch workflow from model export to deployment#center](./how-executorch-works-high-level.png "The three-step ExecuTorch workflow from model export to deployment")
 
 ## What's next?