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.

Welcome to Week 1 of the Jack Righteous Weekly AI Music Strategies. Our mantra here is simple: Create what you love — love what you create. This week isn’t about chasing the “perfect” generation. It's about connecting with something real inside the music. Yes, it’s Valentine’s Day week, so the default theme might feel like romantic love — maybe even sexy love — and that’s totally fine if that’s what speaks to you. But this challenge is really about something deeper. Love for a person. Love for faith. Love for family. Love for healing. Love for hope. Love for a dream you’re chasing. Find something personal that matters to you right now. Stretch a little beyond what’s easy. Let the music and the vocals help you reach that feeling — not just describe it. Your goal is to finish one song that actually moves you. Use the Weekly AI Music Progress Dashboard to track your work and generate your share-back post: https://jackrighteous.com/pages/weekly-ai-music-progress-dashboard (The dashboard helps you capture progress and build a recap to share here.) Important for New Members and Beginners If you’re new to AI music or feeling unsure where to start — you’re in the right place. We’re kicking off our first series of Classroom posts this week, built specifically to walk beginners step by step through: • setting clear creative goals • shaping emotion and style • choosing strong versions • refining songs with intention These will be simple, practical, and easy to follow. If you’re already creating confidently, you’re welcome to jump straight into the weekly challenge — and you’ll also have access to the Classroom lessons for deeper strategy and custom reviews. Helpful Guidance for This Week - Weekly AI Music Strategies Hub https://jackrighteous.com/pages/weekly-ai-music-strategies

Most AI music creators don’t fail because they can’t generate good songs. They fail because the process breaks after generation. Common problems I see: • the best prompt gets lost• versions get messy• creators don’t remember which take was strongest• the song never reaches a “finished” stage So even good songs die in the folder. 🐝 The real shift AI music gets way easier when you treat it like a process: Generate → Save → Compare → Refine → Release Instead of: Generate → Regenerate → Regenerate → Forget Simple habit that changed everything for me The moment I get a good version, I immediately: • save the prompt• label the version clearly• note why it’s strong This alone helped me finish more tracks than any new prompt trick. ⚠️ Beginner trap Chasing “one more generation” instead of improving the strong one you already have. ✅ Creator takeaway Good AI music comes from: Clear prompts + version control + refinement Not endless regenerating. How do you currently keep track of your versions — or do they get messy like mine used to? 👇

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 👇

When you type a prompt into Suno, you’re not just asking Suno to make a song. You’re giving it instructions. Meta tags are simply the words that tell Suno: • what style of music to create• what emotion to focus on• how the vocals should sound• how the song should flow Think of them as musical directions — not random keywords. ❤️ Why love songs are a great example Love songs exist in every genre: Pop love songsAfrobeat love songsCinematic love themesWorship love songs The genre sets the music style.The meta tags guide the feeling and performance. 🎵 Simple example Instead of just writing: Love song Try guiding Suno like this: Pop, romantic, emotional, soft female vocal, slow build, heartfelt chorus Now Suno understands: • the genre• the emotion• the vocal style• the flow ⚠️ Common beginner mistake Using only genre names and wondering why songs feel random. Genre tells Suno what kind of music.Meta tags tell Suno how it should feel and perform. You need both. ✅ Beginner takeaway Think in terms of: Genre + Emotion + Vocal style + Flow This alone will dramatically improve your results. 👉 If you’d like to actually try this with a guided beginner-friendly workflow (saving prompts, tracking versions, and building a love song from start to release), I put together a free Love Song Challenge here:https://jackrighteous.com/blogs/news/create-a-love-song-this-week What emotion do you enjoy creating most right now — love, joy, sad, epic, peaceful, or hype? 👇