3d (edited) • Workflows & Refinement
Artist Profile Deconstruction - Lesson 1 | Page 3 | Task 2
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.
Confirm understanding before proceeding.
What This Does
This keeps the analysis:
professional
high-level
safe to work with
It mirrors how music supervisors, labels, and libraries talk about music internally.
STEP 2 — Emotional Identity → Mood & Energy Inputs
Prompt 2: Emotional Analysis
Analyze the emotional identity of [ARTIST NAME].
Output in TWO SECTIONS:
SECTION A: AI-READY DESCRIPTORS
- Emotional tone
- Energy level (low / medium / high)
- Energy behavior (steady, rising, fluctuating)
- Emotional intensity (restrained / moderate / aggressive)
SECTION B: BEGINNER EXPLANATION
- Explain what each descriptor means in simple terms
- Explain how these traits affect how an AI-generated song will feel
How to Use This
Section A becomes mood and energy guidance for Suno.
Section B helps you understand why those settings matter.
STEP 3 — Song Structure → Form & Flow
Prompt 3: Structural Tendencies
Analyze common song structure tendencies associated with [ARTIST NAME].
Output in TWO SECTIONS:
SECTION A: AI-READY STRUCTURE LOGIC
- Typical intro length (short / medium / long)
- Section clarity (clear / evolving / loose)
- Chorus role (reinforcing / expanding / minimal)
- Ending style (fade, loop, resolved stop)
SECTION B: BEGINNER EXPLANATION
- Explain how these structures help listeners follow the song
- Explain why this structure works for repeated listening
Why This Matters
AI tools respond much better when structure is intentional.
This step helps guide the shape of the music, not just the vibe.
STEP 4 — Arrangement → Density & Support
Prompt 4: Arrangement Translation
Analyze arrangement and production tendencies associated with [ARTIST NAME].
Output in TWO SECTIONS:
SECTION A: AI-READY ARRANGEMENT TAGS
- Density (sparse / moderate / dense)
- Rhythm role (leading / supportive)
- Harmonic complexity (low / medium / high)
- Arrangement behavior (repetitive / evolving / minimal)
SECTION B: BEGINNER EXPLANATION
- Explain what each tag means
- Explain how density and restraint affect clarity
What This Controls
This step influences:
how busy the track feels
how much space exists for vocals or emotion
whether the music feels focused or cluttered
STEP 5 — Vocal Delivery (High-Level, Safe)
Prompt 5: Vocal Approach
Analyze vocal delivery tendencies associated with [ARTIST NAME].
Output in TWO SECTIONS:
SECTION A: AI-READY VOCAL DESCRIPTORS
- Vocal presence (forward / balanced / background)
- Delivery style (spoken / melodic / hybrid)
- Performance intensity (restrained / moderate / expressive)
- Vocal role (message-focused / emotion-focused)
SECTION B: BEGINNER EXPLANATION
- Explain how these traits affect listener perception
- Explain why clarity and restraint often matter
Important Note on Vocals
Vocals carry the highest risk in AI music workflows.
This step is about function, not imitation.
STEP 6 — Remove the Artist (Critical Step)
This step protects originality.
Prompt 6: Transformation
Take all AI-READY DESCRIPTORS from previous steps.
Rewrite them to REMOVE:
- the artist’s name
- any identifiable references
- any cultural or personal markers
Output a neutral style description suitable for Suno or other AI tools.
What You’re Left With
No artist names
No imitation
Clear, usable guidance
This is what gets fed into Suno — not the artist.
What This Exercise Produces (and What It Doesn’t)
It produces:
clarity
vocabulary
control
Suno-ready guidance
It does not produce:
a finished song
a distributable track
a license-ready asset
Those steps come later.
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Artist Profile Deconstruction - Lesson 1 | Page 3 | Task 2
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