docs: restore general Open Training start-here; keep AI Factory

modules/ROOT/nav.adoc

@@ -1 +1,2 @@
-* xref:index.adoc[]
+* xref:index.adoc[Home]
+* xref:ai-factory-accelerator.adoc[AI Factory accelerator]

modules/ROOT/pages/ai-factory-accelerator.adoc

@@ -0,0 +1,42 @@
+= Red Hat & NVIDIA AI Factory Content Accelerator
+:navtitle: AI Factory accelerator
+
+== The Mission: Industrialize Enterprise AI
+
+Welcome to the Virtual AI Factory w/ NVIDIA Content Accelerator. While we won't be in Frankfurt, this intensive, distributed hackathon is designed to bridge the gap between theoretical AI product knowledge and industrial-scale, real-world execution.
+
+In a world where AI can quickly generate code and match patterns, it still lacks the "hard knocks" of real-world trial and error. AI does not inherently understand *why* a specific architecture led to results, or why alternative methods technically work but ultimately fail in production. 
+
+**That is where you come in.** Your true value lies in sharing the lessons learned through failure, iteration, and field experience. Together, we are going to translate your raw expertise into the "field-opinionated" playbooks and technical blueprints required to deliver Red Hat and NVIDIA AI Factory solutions at scale.
+
+== The Engine: Open Training & Giveback Incentives
+
+To rapidly capture and distribute your playbooks, we are utilizing the **Red Hat Open Training framework** as our delivery vehicle. You will build your blueprints using a straightforward "docs-as-code" approach (AsciiDoc and Antora), allowing you to focus purely on technical accuracy while the framework handles the formatting, scaling, and publishing.
+
+**Your Impact & Reward:** By contributing to this accelerator, you are directly empowering internal associates, partners, and customers to replicate your success. Furthermore, your published blueprints are highly visible and eligible for recognition through the **Red Hat Giveback Program** (e.g., earning 4 points for new courses and 3 points for updates).
+
+== Hackathon Goals & Business Impact
+
+To maintain focus and drive immediate customer value, your hackathon builds will target these core objectives. *(Note: Specific tracks and workloads will be finalized and assigned prior to our kickoff on the 15th).*
+
+* **Production-Ready Deployments:** Develop automated Quickstarts and Playbooks (using OpenShift AI and NVIDIA NIM) that provide the field with a clear, repeatable path to production, accelerating time-to-value.
+* **Autonomous Agents:** Build enablement around the Llama Stack API and NVIDIA OpenShell runtime to guide teams in transitioning from simple chatbots to highly secure, industrial-scale AI agents.
+* **High-Performance Serving:** Establish hardware and storage benchmarking standards for NVIDIA-Certified Systems to ensure deployments meet strict AI service level objectives and reduce TCO.
+* **Security & Regulatory Compliance:** Integrate STIG-hardened containers, RHEL, and NVIDIA BlueField DPUs to deliver a zero-trust architecture for mission-critical, regulated industries.
+* **AI-First Delivery & Enablement:** Apply an AI-first delivery approach using "skills" to automate framework delivery, shifting from traditional manual consulting to scalable, AI-assisted field operations for Specialists, Consultants, and Partners.
+
+== The Definition of "Done"
+
+By the end of this hackathon, a successful blueprint submission will include:
+. A completed, AI-assisted **Course Design Document (CDD)** mapping out your technical concepts to actionable tasks.
+. A working hands-on lab environment scenario (targeting the dual H100 baseline—either a step-by-step *How-To* or a troubleshooting *Break-Fix*).
+. An Antora-compliant GitHub repository containing your structured AsciiDoc pages and navigation map.
+
+== Next Steps
+
+Ready to start building? Use the Open Training toolchain so you can focus on the technical story.
+
+* **Step 1:** Follow xref:start_here:ai-assisted-cdd.adoc[AI-Assisted Design Phase (CDD)] to generate a course outline from your notes.
+* **Step 2:** Review formatting guidance under xref:references:asciidocqrg.adoc[References] and create your repo from the https://github.com/RedHatQuickCourses/course-starter-template[course-starter-template,window=_blank].
+* **Step 3:** Author your playbook in AsciiDoc and use the lab guidance under xref:lab:index.adoc[Lab Infrastructure].
+* **General Open Training path:** xref:start_here:workflow.adoc[Getting started] (non-hackathon contributors).

modules/ROOT/pages/index.adoc

@@ -1,41 +1,25 @@
-= Red Hat & NVIDIA AI Factory Content Accelerator
-:navtitle: Event Summary
+= Red Hat Open Training Contributor Guide
+:navtitle: Home
 
-== The Mission: Industrialize Enterprise AI
+== Introduction
 
-Welcome to the Virtual AI Factory w/ NVIDIA Content Accelerator. While we won't be in Frankfurt, this intensive, distributed hackathon is designed to bridge the gap between theoretical AI product knowledge and industrial-scale, real-world execution.
+This guide is the reference for anyone developing or contributing training content through the *Red Hat Open Training* program (Quick Courses built with AsciiDoc and Antora).
 
-In a world where AI can quickly generate code and match patterns, it still lacks the "hard knocks" of real-world trial and error. AI does not inherently understand *why* a specific architecture led to results, or why alternative methods technically work but ultimately fail in production. 
+Open Training helps subject matter experts share knowledge across Red Hat and beyond.
+Program overview and intake live on the PERT Confluence space:
+https://redhat.atlassian.net/wiki/spaces/PERT/pages/291408641[Open Training Program,window=_blank].
 
-**That is where you come in.** Your true value lies in sharing the lessons learned through failure, iteration, and field experience. Together, we are going to translate your raw expertise into the "field-opinionated" playbooks and technical blueprints required to deliver Red Hat and NVIDIA AI Factory solutions at scale.
+A key goal is to enable "citizen developers" and reduce the time required to build and maintain training content.
+Creating content across many products and fast release cycles takes more than one team — this guide and the course starter template help you focus on the technical story while the framework handles structure and publishing.
 
-== The Engine: Open Training & Giveback Incentives
+== Start here
 
-To rapidly capture and distribute your playbooks, we are utilizing the **Red Hat Open Training framework** as our delivery vehicle. You will build your blueprints using a straightforward "docs-as-code" approach (AsciiDoc and Antora), allowing you to focus purely on technical accuracy while the framework handles the formatting, scaling, and publishing.
+. New to Open Training? Follow xref:start_here:workflow.adoc[Getting started].
+. Create a content repository from the https://github.com/RedHatQuickCourses/course-starter-template[course-starter-template,window=_blank] (see that repo README for `course-init.sh`).
+. Need a lab environment? See the xref:lab:index.adoc[Lab Infrastructure Guide].
+. Formatting and AsciiDoc help: xref:references:asciidocqrg.adoc[AsciiDoc quick reference] and related pages under References.
 
-**Your Impact & Reward:** By contributing to this accelerator, you are directly empowering internal associates, partners, and customers to replicate your success. Furthermore, your published blueprints are highly visible and eligible for recognition through the **Red Hat Giveback Program** (e.g., earning 4 points for new courses and 3 points for updates).
+== Featured resource
 
-== Hackathon Goals & Business Impact
-
-To maintain focus and drive immediate customer value, your hackathon builds will target these core objectives. *(Note: Specific tracks and workloads will be finalized and assigned prior to our kickoff on the 15th).*
-
-* **Production-Ready Deployments:** Develop automated Quickstarts and Playbooks (using OpenShift AI and NVIDIA NIM) that provide the field with a clear, repeatable path to production, accelerating time-to-value.
-* **Autonomous Agents:** Build enablement around the Llama Stack API and NVIDIA OpenShell runtime to guide teams in transitioning from simple chatbots to highly secure, industrial-scale AI agents.
-* **High-Performance Serving:** Establish hardware and storage benchmarking standards for NVIDIA-Certified Systems to ensure deployments meet strict AI service level objectives and reduce TCO.
-* **Security & Regulatory Compliance:** Integrate STIG-hardened containers, RHEL, and NVIDIA BlueField DPUs to deliver a zero-trust architecture for mission-critical, regulated industries.
-* **AI-First Delivery & Enablement:** Apply an AI-first delivery approach using "skills" to automate framework delivery, shifting from traditional manual consulting to scalable, AI-assisted field operations for Specialists, Consultants, and Partners.
-
-== The Definition of "Done"
-
-By the end of this hackathon, a successful blueprint submission will include:
-. A completed, AI-assisted **Course Design Document (CDD)** mapping out your technical concepts to actionable tasks.
-. A working hands-on lab environment scenario (targeting the dual H100 baseline—either a step-by-step *How-To* or a troubleshooting *Break-Fix*).
-. An Antora-compliant GitHub repository containing your structured AsciiDoc pages and navigation map.
-
-== Next Steps
-
-Ready to start building? We have streamlined the toolchain so you can bypass the formatting hurdles and get straight to the code.
-
-* **Step 1:** Head over to the **AI-Assisted Design Phase** to put together  and generate your course outline from your raw notes and references.
-* **Step 2:** Review the **Formatting Cheat Sheet** to understand how to structure your repository and add formatting flavor to your content.
-* **Step 3:** Start coding your playbook!
+Building for the Red Hat & NVIDIA AI Factory accelerator?
+See xref:ai-factory-accelerator.adoc[AI Factory Content Accelerator] for hackathon goals, definition of done, and next steps (including the AI-assisted Course Design Document path under Getting Started).

modules/start_here/nav.adoc

@@ -1 +1,3 @@
-* xref:workflow.adoc[]
+* Getting started
+** xref:workflow.adoc[Getting started]
+** xref:ai-assisted-cdd.adoc[AI-assisted CDD (AI Factory)]

modules/start_here/pages/ai-assisted-cdd.adoc

@@ -0,0 +1,62 @@
+= AI-Assisted Design Phase (The CDD)
+:navtitle: Course Design Phase
+
+== Skip the Blank Page Syndrome
+
+As an elite engineer, your time during this hackathon is best spent on technical execution and architecture. 
+
+However, before you start writing AsciiDoc, you need a blueprint. We call this the **Course Design Document (CDD)**. We have an automated script (`skill.md`) that will read this CDD and automatically generate your entire GitHub repository structure, folders, and navigation map for you. 
+
+To ensure the automation works, we ask that your CDD be in the following specific Markdown format.
+
+== Step 1: The AI Generation Prompt
+
+The prompt below contains the specific template. You are paste it into your preferred AI assistant (e.g., ChatGPT, Claude, Red Hat Lightspeed) along with your raw technical notes. 
+
+[source,text]
+----
+You are an expert technical instructional designer. I am going to provide you with my raw notes, terminal outputs, and code snippets for a new Red Hat AI Factory with NVIDIA technical blueprint. 
+
+Your task is to organize my notes into a structured Course Design Document (CDD) using STRICT Markdown formatting. The tone must be highly technical and geared toward Senior Platform Engineers and SREs. Heavily emphasize production-ready deployments, AI-first delivery methodology (using skills), and troubleshooting.
+
+CRITICAL: You must output the document exactly matching the schema below. Do not deviate from these heading names or the table structure, as this output will be parsed by an automated script.
+
+# [Insert Course Title Here]
+
+# COURSE GOAL
+[Write a 2-3 sentence overarching goal for the blueprint]
+
+# TARGET AUDIENCE
+[Define the primary persona, e.g., SRE, Platform Engineer]
+
+# PREREQUISITES
+[List required prior knowledge and tools]
+
+# LAB INFRASTRUCTURE REQUIREMENTS
+[Define hardware, required software, and pre-provisioned state. Note: Target baseline is NVIDIA Launchpad environments with dual H100 nodes unless otherwise specified.]
+
+# COURSE DESIGN
+| Description | Session Type |
+| --- | --- |
+| LEARNING OBJECTIVE #1: [Insert objective here] | |
+| [Insert specific hands-on task or troubleshooting concept] | Lecture |
+| [Insert the specific Lab scenario and Validation Checkpoints] | Lab |
+| LEARNING OBJECTIVE #2: [Insert objective here] | |
+| [Insert specific hands-on task or troubleshooting concept] | Lecture |
+| [Insert the specific Lab scenario and Validation Checkpoints] | Lab |
+
+Here are my raw notes:
+[INSERT YOUR RAW NOTES, CONFIG FILES, AND IDEAS HERE]
+----
+
+== Step 2: Validate the Output & Infrastructure Needs
+
+Once the AI generates your CDD, save it as `prompts/course-design.md` in your repository. Before you run the automation, quickly verify two things:
+
+* **The Table Structure:** Ensure the `Session Type` column strictly uses terms like `Lab` or `Lecture`. This tells the automation which file templates to use.
+
+== Step 3: Auto-Generate Your Repository
+
+With your `course-design.md` file saved, you can now run the initialization script to scaffold your entire course structure automatically!
+
+* **Next Step:** Review the Formating Cheat Sheet ** to see how to format your code blocks and warnings once you start writing your content.