adding summary and faqs

Author: anupras-mohapatra-arm

content/learning-paths/servers-and-cloud-computing/performix-agentic-dynamic-insights-codex/_index.md

@@ -1,7 +1,7 @@
 ---
 title: Generate Arm Performix AI insights in Visual Studio Code with Codex
 
-description: Configure the Arm Performix MCP server for Codex in Visual Studio Code and use profile evidence to generate and validate AI Insights.
+description: Configure the Arm Performix MCP server for Codex in Visual Studio Code and use profile evidence to generate and validate AI insights.
 
 minutes_to_complete: 20
 
@@ -19,9 +19,59 @@ prerequisites:
     - Access to Codex through ChatGPT sign-in, or an organization-approved OpenAI API key provided through the `OPENAI_API_KEY` environment variable.
     - Permission from your organization to share profile data, symbols, source excerpts, disassembly excerpts, and performance metrics with Codex.
 
+# START generated_summary_faq
+generated_summary_faq:
+  template_version: summary-faq-v3
+  generated_at: '2026-07-27T15:24:59Z'
+  generator: ai
+  ai_assisted: true
+  ai_review_required: true
+  model: gpt-5
+  prompt_template: summary-faq-v3
+  source_hash: 3e21e94826b1e02673c240ad88dc503354a1920397903348316d696cf4e2aa2c
+  summary_generated_at: '2026-07-27T15:24:59Z'
+  summary_source_hash: 3e21e94826b1e02673c240ad88dc503354a1920397903348316d696cf4e2aa2c
+  faq_generated_at: '2026-07-27T15:24:59Z'
+  faq_source_hash: 3e21e94826b1e02673c240ad88dc503354a1920397903348316d696cf4e2aa2c
+  summary: >-
+    You'll learn how to connect Codex in Visual Studio Code to the Arm Performix Model
+    Context Protocol (MCP) server, generate an AI insight for a specific Code Hotspots run, and
+    validate the result with profile evidence. After configuring the MCP server on your local machine, you’ll select or create a representative Code Hotspots run, request an AI insight by run ID, and evaluate its supporting evidence.
+    Then, you'll open the same run in Arm Performix to inspect flame graphs, functions, call
+    stacks, source, and disassembly to confirm the recommendation.
+  faqs:
+  - question: How do I confirm Codex is connected to Arm Performix through MCP?
+    answer: >-
+      Ask Codex to list Arm Performix recipes, targets, or supported Code Hotspots runs. A successful
+      listing confirms the MCP server is configured and reachable.
+  - question: Which MCP configuration method should I use, and where should I configure it?
+    answer: >-
+      You can update Codex extension settings, run the `codex mcp add` command, or
+      edit `~/.codex/config.toml`. Configure the MCP on the host where Codex runs; in remote development,
+      verify which machine hosts Codex.
+  - question: What should I prepare before creating a new Code Hotspots run?
+    answer: >-
+      Choose a representative workload and specify the executable with an absolute path. Build the workload with debug symbols when possible and aim for at least 20 seconds of activity to collect
+      enough samples.
+  - question: How do I make sure Codex uses the exact profile I want?
+    answer: >-
+      Request the AI Insight using the run ID. If you don’t know it, ask Codex to list supported
+      Code Hotspots runs with IDs, targets, workloads, and creation times. Then, select the correct
+      ID.
+  - question: What should I check to validate an AI Insight against the profile?
+    answer: >-
+      Open the same run in Arm Performix and inspect the **Flame graph**, **Functions**, **Call Stack**,
+      **Source**, and **Disassembly** views. Confirm that the cited functions, call paths, and sample percentages
+      match the evidence referenced by the insight.
+# END generated_summary_faq
+
 author: 
     - Julie Gaskin
 
+generate_summary_faq: false
+rerun_summary: false
+rerun_faqs: false
+
 ### Tags
 skilllevels: Introductory
 subjects: Performance and Architecture

content/learning-paths/servers-and-cloud-computing/performix-agentic-dynamic-insights-codex/configure_mcp_codex.md

@@ -130,7 +130,7 @@ Record the run ID you want to analyze. A run name can be changed and might not b
 
 ## Troubleshoot the MCP connection
 
-Use the following checks to diagnore MCP connection issues: 
+Use the following checks to diagnose MCP connection issues: 
 
 ### The Arm Performix MCP server isn't listed
 
@@ -178,7 +178,7 @@ If you have several MCP servers, an ambiguous prompt such as `give me insights`
 
 ### Code Hotspots runs are missing
 
-Check:
+Check for the following:
 
 - You created at least one completed Code Hotspots run.
 - The MCP server runs as the same user who created or imported the run.
@@ -198,4 +198,4 @@ If the CLI also returns no run, create or import a run. If the CLI returns the r
 
 You've now configured and tested the MCP connection.
 
-Next, you'll create a Code Hotspots run if no supported run is available. If you already. have a supported run, skip to [Generate Arm Performix AI insights for a Code Hotspots run](/learning-paths/servers-and-cloud-computing/performix-agentic-dynamic-insights-codex/generate_ai_insights/).
+Next, you'll create a Code Hotspots run if no supported run is available. If you already have a supported run, skip to [Generate Arm Performix AI insights for a Code Hotspots run](/learning-paths/servers-and-cloud-computing/performix-agentic-dynamic-insights-codex/generate_ai_insights/).

content/learning-paths/servers-and-cloud-computing/performix-agentic-dynamic-insights-codex/create_a_code_hotspots_run.md

@@ -45,7 +45,7 @@ For example:
 Use Arm Performix to run the Code Hotspots recipe on target "graviton-dev" with workload "/opt/myapp/bin/my_app --input /data/input.dat". Before starting, repeat the target and workload and ask me to confirm them. When the run completes, return its run ID and collection status.
 ```
 
-After you confirm the target, Codex uses the MCP server to start the recipe. Review any permission request before allowing the assistant to run a command on the target.
+After you confirm the target and workload, Codex uses the MCP server to start the recipe. Review any permission request before allowing the assistant to run a command on the target.
 
 ## Create a run with the Arm Performix CLI
 

content/learning-paths/servers-and-cloud-computing/performix-agentic-dynamic-insights-codex/open-in-performix.md

@@ -11,12 +11,12 @@ layout: learningpathall
 
 A chat response helps you triage a performance problem, but the underlying profile is the source of evidence. Open the same run ID in Arm Performix and inspect the views relevant to the claim:
 
-- **Flame graph** to see where sampled CPU time accumulates across call paths.
-- **Functions** to confirm which functions account for the largest sample percentages.
-- **Call Stack** to distinguish a function's own cost from the cost of its callees.
-- **Source** to correlate samples with source lines when source mapping is available.
-- **Disassembly** to inspect generated instructions and confirm claims about the compiled code.
-- **Compare** to evaluate equivalent before-and-after runs.
+- **Flame graph** to see where sampled CPU time accumulates across call paths
+- **Functions** to confirm which functions account for the largest sample percentages
+- **Call Stack** to distinguish a function's own cost from the cost of its callees
+- **Source** to correlate samples with source lines when source mapping is available
+- **Disassembly** to inspect generated instructions and confirm claims about the compiled code
+- **Compare** to evaluate equivalent before-and-after runs
 
 Use the AI insight to focus your investigation, not replace it. For example, if the insight says a loop appears scalar, inspect its disassembly and compiler flags before changing the implementation. If Code Hotspots only shows that the loop is hot, run Instruction Mix or another appropriate recipe before concluding that scalar execution is the cause.