Update RAG documentation: refine titles and section headings for clarity and consistency across setup, pipeline, and memory observation guides.

Author: madeline-underwood

content/learning-paths/laptops-and-desktops/dgx_spark_rag/1_rag.md

@@ -1,12 +1,12 @@
 ---
-title: Understanding RAG on Grace–Blackwell (GB10)
+title: Build a RAG pipeline on Arm-based Grace–Blackwell (GB10) systems
 weight: 2
 
 ### FIXED, DO NOT MODIFY
 layout: learningpathall
 ---
 
-## Before you start
+## Get started
 
 Before starting this Learning Path, you should complete [Unlock quantized LLM performance on Arm-based NVIDIA DGX Spark](/learning-paths/laptops-and-desktops/dgx_spark_llamacpp/) to learn about the CPU and GPU builds of llama.cpp. This background is recommended for building the RAG solution on llama.cpp.
 

content/learning-paths/laptops-and-desktops/dgx_spark_rag/2_rag_setup.md

@@ -1,5 +1,5 @@
 ---
-title: Configure your development environment and prepare models
+title: Configure the RAG development environment and models
 weight: 3
 layout: "learningpathall"
 ---

content/learning-paths/laptops-and-desktops/dgx_spark_rag/2b_rag_setup.md

@@ -1,16 +1,16 @@
 ---
-title: Add documents to the vector database 
+title: Add documents to the RAG vector database 
 weight: 4
 layout: "learningpathall"
 ---
 
-## Prepare a sample document corpus
+## Prepare a sample document corpus for RAG
 
 You are now ready to add your documents to the RAG database that will be used for retrieval and reasoning. 
 
 This converts your raw knowledge documents into clean, chunked text segments that can later be vectorized and indexed by FAISS.
 
-## Understanding FAISS for vector search
+## Use FAISS for efficient vector search on Arm
 
 FAISS (Facebook AI Similarity Search) is an open-source library developed by Meta AI for efficient similarity search and clustering of dense vectors. It's particularly well-suited for RAG applications because it can quickly find the most relevant document chunks from large collections.
 
@@ -21,7 +21,7 @@ Key advantages of FAISS for this application:
 - Speed: Uses advanced indexing algorithms to perform nearest-neighbor searches in milliseconds
 - Flexibility: Supports multiple distance metrics (L2, cosine similarity) and index types
 
-### Create a workspace and data folder
+## Set up youe RAG workspace and data folder
 
 Create a directory structure for your data:
 
@@ -57,7 +57,7 @@ Use `wget` to batch download all the PDFs into `~/rag/pdf`.
 wget -P ~/rag/pdf -i datasheet.txt
 ```
 
-### Convert PDF into txt file
+## Convert PDF documents to text files
 
 Then, create a Python file named `pdf2text.py` with the code below:
 
@@ -109,7 +109,7 @@ At the end of the output you see:
 Total converted PDFs: 12
 ```
 
-### Verify your corpus
+## Verify your document corpus
 
 You should now see a number of files in your folder. Run the command below to inspect the results: 
 
@@ -119,7 +119,7 @@ find ~/rag/text/ -type f -name "*.txt" -exec cat {} + | wc -l
 
 It will show how many lines are in total. The number is around 100,000.
 
-## Build an Embedding and Search Index
+## Build an embedding and search index with FAISS
 
 Convert your prepared text corpus into vector embeddings and store them in a FAISS index for efficient semantic search.
 
@@ -133,7 +133,7 @@ This stage enables your RAG pipeline to retrieve the most relevant text chunks w
 
 Use e5-base-v2 to encode the documents and create a FAISS vector index.
 
-### Create the FAISS builder script
+### Create and run the FAISS builder script
 
 
 ```bash

content/learning-paths/laptops-and-desktops/dgx_spark_rag/3_rag_pipeline.md

@@ -1,10 +1,10 @@
 ---
-title: Implementing the RAG pipeline
+title: Build and run the RAG pipeline on Arm-based Grace–Blackwell (GB10) systems
 weight: 5
 layout: "learningpathall"
 ---
 
-## Integrating retrieval and generation
+## Integrate retrieval and generation on Arm
 
 In the previous sections, you prepared the environment, validated the e5-base-v2 embedding model, and verified that the Llama 3.1 8B Instruct model runs successfully on the Grace–Blackwell (GB10) platform.
 

content/learning-paths/laptops-and-desktops/dgx_spark_rag/4_rag_memory_observation.md

@@ -1,5 +1,5 @@
 ---
-title: Observe unified memory performance
+title: Monitor unified memory performance on Arm-based Grace–Blackwell systems
 weight: 6
 layout: "learningpathall"
 ---
@@ -25,7 +25,7 @@ Open two terminals on your GB10 system and use them as listed in the table below
 You should also have your original terminals open that you used to run the `llama-server` and the RAG queries in the previous section. You will run these again and use the two new terminals for observation.
 
 
-### Prepare for Unified Memory Observation Experiments
+### Prepare for unified memory observation
 
 Ensure the RAG pipeline is stopped before starting the observation.
 
@@ -200,7 +200,7 @@ The GPU executes compute kernels with GPU utilization at 96%, without reading fr
 
 The `utilization.memory=0` and `memory.used=[N/A]` metrics are clear signs that data sharing, not data copying, is happening.
 
-### Observe and interpret unified memory behavior
+### Interpret unified memory behavior
 
 This experiment confirms the Grace–Blackwell Unified Memory architecture in action:
 - The CPU and GPU share the same address space.