I came across a very interesting idea from the author まじん (Majin) on note.com:

• Original version of the prompt: https://note.com/majin_108/n/n39235bcacbfc

• Updated and improved version: https://note.com/majin_108/n/nd11d1f88a939

Majin used Gemini to turn a single prompt into a complete Google Slides presentation, but I tried customizing it to run with ChatGPT (or Google AI Studio), and the results were quite exciting.

1. 🔍 Structure of Majin’s Prompt

Through analysis, Majin’s prompt has the following main components:

• Role assignment for AI: The AI is not just a chatbot, but acts as a Data Scientist and Presentation Designer.

• Absolute mission: From a piece of input text, the AI must output a JavaScript object array (slideData) that defines the entire slide structure.

• Fixed blueprint: The prompt includes a Google Apps Script (GAS) framework, where you only need to replace slideData to generate Google Slides.

• SlideData includes:

  • title: Slide title

  • content: Content (in the form of bullets, text, or tables)

  • pattern: Display type (Title, List, TwoColumn, Image, …)

  • speakerNote: Presenter’s notes

👉 The important point: The prompt does not directly create slides, but outputs JSON-like data that the GAS script uses to build the slides.

2. 🛠️ Customizing & Running with ChatGPT

First, download system prompt.md

🔧 Steps inside the Project

1. Create a new Project → give it a name (e.g., Google Slide Auto Generator).

2. Add a file → upload system prompt.md.

3. In the Project Settings / Overview screen, you will find the Instructions (System message) box → paste the line:
   添付した「system prompt.md」に忠実に従ってください。

4. (Meaning: Please strictly follow the attached system prompt.md).
   If you don’t see a dedicated “Instructions” field, ChatGPT may show “Description” or “Setup” instead → paste it there. It works as the System Prompt for the whole Project.

5. From now on, in that Project, whenever you input raw text, ChatGPT will immediately reference system prompt.md and output both slideData + GAS code.

👉 In other words: the Project’s Instruction acts as a dedicated System Message for that workspace. The UI may look different, but it’s always under “Overview/Setup” when you open the Project.

Next, type a prompt about the content you want to make slides from → and it will generate slideData.

I tested it with content about LLMs and got the following result:

const slideData = [

{

type: ‘title’,

title: ‘The Busy Person’s Intro to Large Language Models’,

date: ‘2025.09.21’,

notes: ‘Re-recorded overview of LLMs focusing on what they are, how to run them, how they are trained, and why next-word prediction works’

},

{

type: ‘content’,

title: ‘Agenda’,

points: [

‘What is an LLM’,

‘Running a model locally’,

‘Training at scale’,

‘Why next-word prediction is powerful’,

‘Key takeaways’

],

notes: ‘Keep it practical and concrete. We start with the simplest mental model, then show inference vs training, then the intuition for why LLMs encode knowledge’

},

{

type: ‘section’,

title: ‘1. What is an LLM’,

notes: ‘Define an LLM at the simplest level and frame the talk around artifacts you can hold on disk’

},

{

type: ‘content’,

title: ‘LLM = Two Files’,

subhead: ‘Weights + Runner’,

points: [

‘Parameters file holds learned weights’,

‘Runner implements forward pass of the network’,

‘Self contained package runs offline’

],

notes: ‘Stress that inference can be simple and local. The interesting part lives in the parameters learned during training’

},

{

type: ‘content’,

title: ‘Example: Llama 2 • 70B’,

subhead: ‘Open weights from Meta’,

points: [

‘Sizes include 7B 13B 34B 70B’,

‘Open weights and paper enable local study’,

‘Closed APIs trade control for convenience’

],

notes: ‘Contrast open weights with closed APIs like hosted assistants without exposing internals’

},

{

type: ‘section’,

title: ‘2. Running a Model’,

notes: ‘Give a feel for local inference and why the demo used a smaller model for speed’

},

{

type: ‘process’,

title: ‘Local Inference Flow’,

subhead: ‘From files to generations’,

steps: [

‘Get weights file on disk’,

‘Compile or install a runner’,

‘Point runner to weights’,

‘Send a prompt’,

‘Stream generated tokens’

],

notes: ‘Mention that a 7B model is fast enough on a laptop while 70B is roughly an order of magnitude slower’

},

{

type: ‘compare’,

title: ‘Open Weights vs Closed API’,

leftTitle: ‘Open Weights’,

rightTitle: ‘Closed API’,

leftItems: [

‘Full local control’,

‘Offline capable’,

‘Tunable and inspectable’

],

rightItems: [

‘Best quality access’,

‘No infra to manage’,

‘Faster to integrate’

],

notes: ‘There is no universal winner. Choose based on control privacy latency and capability needs’

},

{

type: ‘section’,

title: ‘3. Training at Scale’,

notes: ‘Explain where weights come from and why training is expensive compared to inference’

},

{

type: ‘content’,

title: ‘Training Ingredients’,

subhead: ‘Data Compute Cost’,

points: [

‘Large curated text corpus’,

‘Massive GPU clusters for days’,

‘Significant budget for runs’

],

notes: ‘Use order of magnitude thinking rather than exact numbers. Emphasize engineering and data work beyond raw compute’

},

{

type: ‘timeline’,

title: ‘From Corpus to Weights’,

subhead: ‘Very high level lifecycle’,

milestones: [

{ label: ‘Collect and filter data’, date: ‘Step 1’, state: ‘done’ },

{ label: ‘Train on GPU cluster’, date: ‘Step 2’, state: ‘done’ },

{ label: ‘Evaluate and align’, date: ‘Step 3’, state: ‘done’ },

{ label: ‘Release weights and runner’, date: ‘Step 4’, state: ‘next’ }

],

notes: ‘Keep the audience at the bird’s eye view. The output is a weights artifact you can reuse everywhere for inference’

},

{

type: ‘section’,

title: ‘4. Why It Works’,

notes: ‘Bridge from mechanics to intuition about capability’

},

{

type: ‘content’,

title: ‘Next Word Prediction Objective’,

subhead: ‘Simple objective rich consequences’,

points: [

‘Model predicts a distribution over next token’,

‘Good prediction forces world knowledge’,

‘Patterns styles and relations get compressed’

],

notes: ‘Relate prediction to lossy compression intuition. To predict the next token the model must encode structure facts and relationships’

},

{

type: ‘content’,

title: ‘Common Misconceptions’,

points: [

‘It is not copy paste of the web’,

‘Bigger is not always better’,

‘Closed is not always better than open’

],

notes: ‘Clarify generalization vs memorization and the role of data quality and algorithms along with scale’

},

{

type: ‘section’,

title: ‘5. Takeaways’,

notes: ‘Land the plane with pragmatic guidance’

},

{

type: ‘content’,

title: ‘Practical Guidance’,

points: [

‘Choose model size for latency and cost’,

‘Use open weights for control’,

‘Use hosted APIs for frontier quality’

],

notes: ‘Map choices to product constraints such as privacy cost latency and maintainability’

},

{

type: ‘closing’,

notes: ‘Thanks for watching. Invite questions and link resources in the description’

}

];

Next steps

1. Open the .md file on your computer.

2. Copy the code from the .md file.

3. Open Google Slides → create a new slide deck.

4. Click Extensions → Apps Script.

5. Paste the copied code into the Apps Script editor.

6. Paste your slideData.

7. Press Run

3. ✅ Experience & Results

• Works well on ChatGPT: No need for Gemini, GPT-5 is enough.

• Advantage: The prompt standardizes the output into a JSON-like object, making it easy to control.

• Reference implementation:

  • Example Google Apps Script project on GitHub: generate-slide-appscript-example

  • Example generated Google Slides: Demo Slide Deck

📌 Conclusion

• Majin’s prompt is a great framework to turn AI into an automatic slide design tool.

• It doesn’t have to be Gemini — ChatGPT (GPT-5) also works well.

• You just need to customize the input → and you can generate Google Slides for any topic (training, pitching, learning…).

👉 This article was written with reference to blogs by まじん (Majin):

• Note.com – Googleスライドが一瞬で完成する“奇跡”のプロンプト

• Note.com – 改良版まじん式プロンプト