SEO optimization

Author: madeline-underwood

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/1-overview.md

@@ -1,5 +1,6 @@
 ---
-title: Overview
+title: Run LLMs locally on Raspberry Pi 5 for Edge AI
+
 weight: 2
 
 ### FIXED, DO NOT MODIFY
@@ -12,7 +13,7 @@ This Learning Path walks you through deploying an efficient large language model
 
 You will create a fully local, privacy-first smart home system that leverages the strengths of Arm Cortex-A architecture. The system can achieve 15+ tokens per second inference speeds using optimized models like TinyLlama and Qwen, while maintaining the energy efficiency that makes Arm processors well suited for always-on applications.
 
-## Why Arm Cortex-A for edge AI?
+## Why Arm Cortex-A76 makes Raspberry Pi 5 ideal for Edge AI
 
 The Raspberry Pi 5's Arm Cortex-A76 processor can manage high-performance computing tasks like AI inference. Key architectural features include:
 
@@ -23,7 +24,7 @@ The Raspberry Pi 5's Arm Cortex-A76 processor can manage high-performance comput
 
 These characteristics make the Raspberry Pi 5 well suited for workloads like smart home assistants, where responsiveness, efficiency, and local processing are important. Running LLMs locally on Arm-based devices brings several practical benefits. Privacy is preserved, since conversations and routines never leave the device. With optimized inference, the system can offer responsiveness under 100 ms, even on resource-constrained hardware. It remains fully functional in offline scenarios, continuing to operate when internet access is unavailable. Developers also gain flexibility to customize models and automations. Additionally, software updates and an active ecosystem continue to improve performance over time.
 
-## Arm ecosystem advantages
+## Leverage the Arm ecosystem for Raspberry Pi edge AI
 
 For the stack in this setup, Raspberry Pi 5 benefits from the extensive developer ecosystem:
 
@@ -45,7 +46,7 @@ The table below shows inference performance for several quantized models running
 | gemma2:2b           | 4.1        | 23,758           |
 | deepseek-r1:7b      | 1.6        | 64,797           |
 
-### Insights
+### LLM benchmark insights on Raspberry Pi 5
 
 - Qwen 0.5B and TinyLlama 1.1B deliver fast token generation and low average latency, making them suitable for real-time interactions such as voice-controlled smart home commands
 - DeepSeek-Coder 1.3B and Gemma 2B trade some speed for improved language understanding, which can be useful for complex tasks or context-aware prompts
@@ -69,4 +70,4 @@ Additionally, the platform must meet the following requirements:
 - Python 3.8 or newer
 - Ability to run [Ollama](https://ollama.com/)
 
-Continue to the next section to start building a smart home system that highlights how Arm-based processors enable efficient, responsive, and private AI applications at the edge.
+In the next section, you’ll set up the software dependencies needed to start building your privacy-first smart home system on Raspberry Pi 5.

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/2-software-dependencies.md

@@ -1,11 +1,15 @@
 ---
-title: Set up software dependencies
+title: Set up software dependencies on Raspberry Pi 5 for Ollama and LLMs
 weight: 3
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
+## Overview
+
+In this section, you’ll prepare your Raspberry Pi 5 by installing Python, required libraries, and Ollama, so you can run large language models (LLMs) locally.
+
 {{% notice Note %}}
 This guide assumes you have set up your Raspberry Pi with Raspberry Pi OS and network connectivity. For Raspberry Pi 5 setup help, see: [Raspberry Pi Getting Started](https://www.raspberrypi.com/documentation/)
 {{% /notice %}}
@@ -26,7 +30,7 @@ Replace `<user>` with your Pi's username (typically `pi`), and `<pi-ip>` with yo
 ssh <user>@<pi-ip>
 ```
 
-## Set up dependencies
+## Install Python and system dependencies
 
 Create a directory called `smart-home` in your home directory and navigate into it:
 
@@ -77,7 +81,7 @@ If installation was successful, the output should be similar to:
 ollama version is 0.11.4
 ```
 
-## Download and test a language model
+## Run a test LLM with Ollama on Raspberry Pi 5
 
 Ollama supports various models. This guide uses `deepseek-r1:7b` as an example, but you can also use `tinyllama:1.1b`, `qwen:0.5b`, `gemma2:2b`, or `deepseek-coder:1.3b`.
 

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/3-test-gpio.md

@@ -1,5 +1,5 @@
 ---
-title: Test GPIO pins
+title: Test Raspberry Pi 5 GPIO pins for smart home devices
 weight: 4
 
 ### FIXED, DO NOT MODIFY
@@ -8,7 +8,7 @@ layout: learningpathall
 
 The next step is to test the GPIO functionality. In this section, you configure an LED light to simulate a smart home device.
 
-## Verify GPIO functionality
+## Verify GPIO setup on Raspberry Pi 5
 
 Gather your electronic components. Connect the anode (long leg) of an LED in series with a 220Ω resistor to GPIO 17 (physical pin 11). Connect the cathode (short leg) to a ground (GND) pin. See the image below for the full setup:
 

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/4-smart-home-assistant.md

@@ -1,11 +1,11 @@
 ---
-title: Smart Home Assistant
+title: Build and Run a Smart Home Assistant on Raspberry Pi 5 with LLMs
 weight: 5
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
-## About the assistant
+## Understand the Smart Home Assistant
 
 In this section, you will run the assistant through the `smart_home_assistant.py` script. It initializes all configured smart devices on specific GPIO pins and starts a local web server for interacting with the assistant. The script processes user commands using a local language model (via Ollama), parses the model’s JSON output, and executes actions such as toggling lights or locking doors. It supports both terminal and web-based control.
 
@@ -16,7 +16,7 @@ git clone https://github.com/fidel-makatia/EdgeAI_Raspi5.git
 cd EdgeAI_Raspi5
 ```
 
-## Connect further hardware
+## Connect additional smart home hardware on Raspberry Pi GPIO pins
 
 In the previous section, you configured a LED on GPIO pin 17. The smart home assistant is by default associating this with a `living_room_light` device. The single LED setup is enough to run through this Learning Path. If you'd like to connect actual devices, or play with more mock sensors, the default configuration looks like the table below. You can repeat the steps on the previous page to verify the hardware setup on the different GPIO pins. See the image below for an example.
 

content/learning-paths/embedded-and-microcontrollers/raspberry-pi-smart-home/_index.md

@@ -3,7 +3,7 @@ title: Build a Privacy-First LLM Smart Home on Raspberry Pi 5
 
 minutes_to_complete: 45
 
-who_is_this_for: This is an introductory topic for developers interested in building smart home systems using on-device LLMs and Arm-based edge platforms like the Raspberry Pi 5.
+who_is_this_for: This is an introductory topic for edge AI developers, Raspberry Pi hobbyists, and software engineers who want to build privacy-first smart home assistants. You’ll learn how to run large language models (LLMs) locally on the Raspberry Pi 5 using Ollama, control GPIO-connected devices, and deploy a web-based assistant without relying on cloud services.
 
 learning_objectives:
   - Understand how the Arm architecture enables efficient, private, and responsive LLM inference
@@ -14,8 +14,8 @@ learning_objectives:
 
 prerequisites:
   - An Arm-based single board computer (for example, Raspberry Pi 5 running Raspberry Pi OS)
-  - Basic electronic components - breadboard, LEDs, resistors, and jumper wires
-  - Basic understanding of Python, GPIO pins, and electronics
+  - Electronic components (breadboard, LEDs, resistors, jumper wires) for GPIO testing
+  - Familiarity with Python programming, Raspberry Pi GPIO pinout, and basic electronics
 
 author: Fidel Makatia Omusilibwa