Merge pull request #1761 from jasonrandrews/review

Review deploy an AI agent on Arm with llama-cpp-agent Learning Path

Author: jasonrandrews

content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/_index.md

@@ -13,7 +13,7 @@ learning_objectives:
 
 prerequisites:
     - An [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server.
-    - Basic understanding of Python and Prompt Engineering.
+    - Basic understanding of Python and prompt engineering.
     - Understanding of LLM fundamentals.
 
 author: Andrew Choi

content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/agent-output.md

@@ -153,7 +153,11 @@ Please write your prompt here:
 
 When you are presented with `Please write your prompt here:` test it with an input prompt. 
 
-Enter `What is the current time?`
+Enter:
+
+```console
+What is the current time?
+```
 
 As part of the prompt, a list of executable functions is sent to the LLM, allowing the agent to select the appropriate function:
 

content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/ai-agent-backend.md

@@ -6,10 +6,12 @@ weight: 4
 layout: learningpathall
 ---
 
-## Python Script for executing an AI Agent application
-With `llama.cpp` built and the Llama3.1 8B model downloaded, you are now ready to create a Python script to execute an AI agent application:
+## Python script for executing an AI Agent application
+
+With `llama.cpp` built and the Llama3.1 8B model downloaded, you are now ready to create a Python script to execute an AI agent application.
+
+Use a text editor to create a Python file named `agent.py` with the content shown below:
 
-Create a Python file named `agent.py` with the content shown below:
 ```bash
 from enum import Enum
 from typing import Union

content/learning-paths/servers-and-cloud-computing/ai-agent-on-cpu/set-up.md

@@ -8,9 +8,9 @@ layout: learningpathall
 
 ## Before you begin
 
-This Learning Path demonstrates how to build an AI Agent Application using open-source LLMs optimized for Arm architecture. The AI Agent can use Large Language Models (LLMs) to perform actions by accessing tools and knowledge. 
+This Learning Path demonstrates how to build an AI Agent Application using open-source LLMs optimized for the Arm architecture. The AI Agent can use Large Language Models (LLMs) to perform actions by accessing tools and knowledge. 
 
-The instructions in this Learning Path have been designed for Arm servers running Ubuntu 22.04 LTS. You require an Arm server instance with at least 4 cores and 16GB of memory to run this example. Configure disk storage up to at least 32 GB. The instructions have been tested on an AWS EC2 Graviton3 `m7g.xlarge` instance.
+The instructions in this Learning Path have been designed for Arm servers running Ubuntu 22.04 LTS. You will need an Arm server instance with at least 4 cores and 16GB of memory. Configure disk storage for 32 GB or more. The instructions have been tested on an AWS EC2 Graviton3 `m7g.xlarge` instance.
 
 ## Overview