I want to make something clear as I step back into this community and start engaging here again now. Over the last 2 months, I was contracted to work on a training program for a music production school. That took a major share of my time on the training content side. But it also pushed me to develop stronger material, sharper systems, and better teaching structure. A lot of that work has already been infused into the training content on my site, and I am now bringing that same level of clarity and development back into this Skool community as well. My website training has gone deep on Suno because that is the AI music platform I have spent the most time testing, building with, and systemizing. But this Skool community is meant to be broader than one tool. This space is for AI music creators in general who want help developing better ideas, stronger songs, cleaner workflows, better release thinking, and more useful creator systems. So here is how I will be handling content in this group moving forward: - If a lesson applies across multiple AI music tools, I will teach it that way. - If a lesson is Suno-specific, I will label it clearly. - If something comes from my deeper site training, I may adapt it here so the lesson is more broadly useful inside this community. That means you do not need to be a Suno-only user to benefit from what I post here. At the same time, I am not going to pretend every AI music tool works the same way. When the details matter, I will say so directly. What I want from you right now Drop a comment below and tell me: 1. What AI music tool or tools you are using 2. What your biggest bottleneck is right now 3. What kind of help you want most from this community next Examples: - better song quality - better lyrics - better prompting - workflow help - release strategy - branding and positioning - turning ideas into finished work I am ready to start building with this community again now, and your feedback will help shape what I post first.

Welcome! 👋This community is about building real AI music projects — not just random generations. Take 1 minute to introduce yourself so we can support each other better. Copy and fill in: Name / creator name: AI music tool you use (Suno, etc.): Main genre or style: What you’re working on right now: What you want help with most: Link to a track (optional): I’ll start in the comments 👇

Next week we’re launching our first class, focused on helping you stop random AI song generation and start creating music with real direction and intention. In Week 1, you’ll learn how to: • Start with a song you love (or one you’ve created) • Break down style, vibe, and emotion behind great tracks • Use AI tools like ChatGPT (or similar) to analyze what works • Turn emotions into song ideas and lyrics • Create an intentional finished track using Suno (or your AI music tool) By the end of the week, you’ll have: ✔ a clearer musical style ✔ emotional direction for your songs ✔ multiple creative ideas ✔ one intentional track you’re proud of ✔ control over your AI music process 📌 Bonus topic added to Week 1 We’re also including a real-world AI music case study on the “I Run” controversy and what creators can learn from it. If you’d like to preview the story before class, here’s the breakdown: 👉 https://jackrighteous.com/blogs/news/i-run-ai-song-controversy-creator-lessons We’ll use it to talk about: • AI music rights • platform takedowns • branding and identity risks • protecting your work moving forward More details coming with the class drop next week

Lesson 1 | Page 3 | Task 3 From Analysis → Your Own Creative Identity (Training Use Only) Important – Please Read First This task is about developing your own artist profile with custom meta tags, not about using a PERSONA. You will use the outputs from: Task 1 — Song Profile Task 2 — Artist Profile Deconstruction to begin shaping your own creative identity in a way that is: intentional informed adaptable over time safe to work with as AI tools evolve Nothing produced in this task is meant to be directly distributed, licensed, or monetized. Guidance on creation, refinement, and risk comes next in Lesson 1, Page 4. Why This Task Exists Artists have always studied other artists. Long before AI, musicians learned by: breaking down songs they loved understanding how arrangements worked noticing how emotion was carried recognizing why certain structures held attention What has changed is speed and accessibility, not the process itself. AI doesn’t replace this work — it amplifies whatever understanding you bring to it. If you don’t know what to listen for, what to extract, or what actually shapes a sound, AI will still generate music — but you won’t know when it’s drifting, flattening nuance, or making choices that don’t serve you. This task exists to give you language, structure, and custom tagging skills so your growth is deliberate — not accidental. What This Task Is Building By the end of this task, you are not building a “style.” You are building: a working artist profile a documented creative point of view a reference you can return to as your skills grow a foundation that can evolve over months and years This profile will change. That’s expected. It can even be used by type of music or emotion/vibe. What You’ll Need Before Starting You must have completed: Task 1 — Song Profile (production extraction from a known reference track) Task 2 — Artist Profile Deconstruction

From Real Artists → Suno-Ready Inputs (Training Use Only) Important – Please Read First This exercise is for training and skill development only. You will analyze real artists using publicly available information, but the results of this exercise are not meant to be used directly in songs you plan to distribute, license, or monetize. This workflow teaches how to study artists safely, translate observations into AI-ready language, and then modify that language to help develop your own sound. Guidance on how to move from analysis to compliant, distributable music is covered later in Lesson 1, Page 4. Why This Exercise Exists Many people misuse AI music tools by typing an artist’s name into a prompt and hoping for the best. That approach often leads to: unoriginal results legal risk and weak creative growth This exercise shows a better way. Instead of copying an artist, you will learn how to: analyze public, observable traits translate those traits into neutral, technical language and use that language to guide AI tools like Suno without imitation Think of this as learning the structure and behavior behind a style, not the surface sound. What You’ll Need Before Starting The name of a real, well-known artist Access to ChatGPT (or a similar tool) An understanding that this is an analysis step, not a creation step STEP 1 — Set Safe Boundaries for Analysis Before analyzing any artist, set clear boundaries so the AI stays in analysis mode and doesn’t drift into copying. Prompt 1: Analysis Setup You are acting as a professional music analysis assistant and AI music prompt engineer. I will provide the name of a real, well-known artist. Your task is to: 1. Analyze only publicly observable, high-level characteristics 2. Translate those characteristics into neutral, AI-music-ready descriptors 3. Avoid imitation, copying, or stylistic cloning Do NOT: - reference specific songs - generate lyrics or melodies - suggest "sounds like [artist]" All outputs must be suitable for transformation into Suno or similar AI tools.