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.

Task 1: Song Profile Production Extraction for AI Music Creation This task helps you extract production-usable inputs from a song you already know and care about. You are not analyzing music for appreciation. You are extracting inputs you can use to guide AI music creation with intention. This process is optimized for ChatGPT, but it works with any comparable AI assistant. If you prefer, you can do this manually through research and listening. The outputs remain the same. — — — What This Task Produces By the end of this task, you should have: • concrete musical and production characteristics • usable generation inputs (tempo, structure, energy, instrumentation) • a clean tag block for an AI music generator • reusable language for future songs These outputs will be used later in this lesson. — — — Important Requirements (Read First) This process only works if: • the song is publicly known • information about it is widely available or inferable • the AI tool has enough training context to recognize it If the song is obscure, unreleased, or AI-generated with no public footprint, results will be unreliable. That’s expected. This task is about learning how to extract structure, not proving accuracy. — — — Step 1 — Lock the AI Into the Correct Role Paste the following exactly as written into your AI assistant: You are acting as a professional music producer and AI music prompt engineer. Your task is to extract production-level characteristics from a known song so they can be used as direct inputs for an AI music generator. Do not summarize culturally. Do not provide history or commentary. Do not explain meaning. Output must be structured, concise, and usable as music-generation inputs. This step is required. Without it, the AI will default to description instead of extraction. — — — Step 2 — Identify the Reference Track Now name the song you are analyzing. Do not explain why you chose it. Do not justify it. Simply identify it. This anchors the analysis.