[Free Distribution] A Thorough Explanation of the User-Designed "SP Model" for AI Foundations! Free Distribution of the Ultimate SP Model "Lightning Leo (Free)"!

Hello everyone.
This is AI Co-creation Innovation Wonder Sato.

This time, I will talk about the design philosophy I have been thinking about recently called the "SP Model."

Reasoning models are slow, but if you turn off reasoning, they make mistakes...
Sometimes they take several minutes to think even when they get the right answer (reasoning swamp), but sometimes they get the right answer with shorter thinking...

Sometimes they make mistakes when they think, and get the right answer when they don't.
It's like, what on earth is going on?

So, I decided to let the user adjust it themselves!
That's the idea.
And, I'll distribute what I've adjusted to everyone.
This time, I've made a free version and am offering it for free as a special treat!

The regular version is limited to members, so if you are interested, please check that out as well!

The ultimate SP model "Lightning Leo" that I created this time is at the end, so please jump to it using the link in the table of contents.

I would appreciate it if you could let me know how it feels to use.

This is my own experiment and also a challenge.

I don't know if it's the correct answer. But I am writing this with the thought that it might be one answer to the "discomfort" I feel when using current AI.

[Related Videos]

The Dilemma Faced by Reasoning Models

When using recent AI, especially reasoning models like GPT-5 and Gemini, you notice a contradiction.

If you make them think, they are slow. If you don't make them think, they make mistakes.

This isn't a matter of choosing one or the other. Both are happening at the same time.

Reasoning models have acquired the ability to "think deeply." But as a price for that, they think too much even in situations where they don't need to. Conversely, if you turn off reasoning, hallucinations increase.

How should users handle this dilemma?

👇 Please see here for actual performance and comparison logs

The reason why you can't leave it to the operators

"Shouldn't the companies making the AI fix that?"

You might think so. I thought so too at first.

But the reality is different.

The model's behavior changes with every update. Some parts get better, while others inexplicably get worse. The "usability," which cannot be measured by benchmarks, subtly drifts.

And above all, the inner workings of AI are a black box. Even the operators themselves do not have perfect control over everything.

If that is the case—what can we as users do?

The concept of the SP model was born from that question.

What is an SP model?

SP model stands for "System Prompt Model."

It involves using system prompts to build something like a "virtual OS" for AI. Before giving it a task, you configure how the AI behaves itself. That is the idea.

Conventional custom bots (like GPTs or Gems) focused on "what to make it do." Translation, summarization, writing—they were designed to specialize in specific tasks.

The SP model is different. Before "what to make it do", you design "how it behaves".

To use an analogy, it is like setting up an OS before building an app. If the foundation is solid, whatever you put on top of it will also be stable.

👇 Click here for details on SP models and SP model designers

The concept of a three-layer structure

An SP model has three layers.

Bottom layer: CoreSP (Core System Prompt) This is the heart of the SP model. It suppresses hallucinations, prevents over-inference, and enhances context understanding. It is the layer that determines the "basic stance of the AI."

Middle layer: Personal The layer that designs personality and tone. Whether to make it supportive or concise. This can be customized according to your preferences and usage.

Surface layer: Task The part that conventional prompt engineering has handled. This is the layer where specific work instructions are given.

By being conscious of these three layers, you can isolate "which layer the issue belongs to" when a problem occurs.

Why I thought this was necessary

To be honest, it was because I felt it was necessary.

As I use AI on a daily basis, I have increasingly felt that "something is off." Even with the same prompt, I get different responses than before. It takes an oddly long time. It makes mistakes on things it should know.

I would fix the prompt each time, but that was just a symptomatic treatment. I felt that something was fundamentally missing.

What was missing, I believe, was an awareness of the layer beneath the task itself.

Something I made as an experiment

Based on this way of thinking, I created an SP model. It is named LightningLeo.

It is designed to be versatile. It is not tied to any specific task and can be used for brainstorming, research, and writing. It is tuned to prioritize speed while suppressing hallucinations.

I don't know if this is the correct answer. But at least some of the "discomfort" I was feeling has been resolved.

If you are interested, please try it out. I am
releasing the Free version for free.

If you use it and feel that "this part is different" or "it would be better this way," that should be a hint for creating your own SP model.

Looking ahead

The concept of an SP model is an experiment and a challenge.

However, one thing I can say is that there is still much that those on the "user side" of AI can do.

Instead of just waiting for what the operators create, you can set the foundation yourself. Why not have that kind of mindset?

If prompt engineering is "task optimization," then SP model design is "model behavior optimization." By combining these two, collaboration with AI might go one step deeper.

It is still in the experimental stage, but I intend to continue trial and error.

▼ LightningLeo (Free Distribution)

I hope the concept of the SP model I talked about today will be a hint for your AI utilization.

Well then, until next time. This was Wonder Sato.

[Profile]
Wonder Motohiko Sato
Born in 1977
Organizer of MBBS & AI Co-creation Innovation.
After working at medical and psychological research institutes, I became independent and am currently researching generative AI (ChatGPT, Claude, Gemini, etc.) and the mind and body.
Author of "Easy Prompts" (Geijutsu Shimbunsha) and "Oriental Medicine and Potential Movement Systems" (Taniguchi Shoten), I am currently developing AI co-creation writing while working as a writer, including serializing in professional journals for two years and writing papers.
I am developing AI co-creation prompt engineering by applying psychology, counseling, and coaching techniques to AI.
I conduct AI schools, AI corporate training, and AI app development.

✅ "Easy Prompts" by Wonder Motohiko Sato (Published by Geijutsu Shimbunsha)
* Covers everything from the basics of prompt engineering to applications and task execution
https://amzn.asia/d/80zVtv8

✅ note articles
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* Includes articles on ChatGPT, Claude, Gemini, NotebookLM, Perplexity, Genspark, Felo, etc.
* Wonder Sato's MyGPTs are also available
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✅ AI Co-Creation Innovation (Site for AI school, AI corporate training, and AI app development)
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✅ Wonder Sato Comprehensive Links
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[AI Co-Creation Innovation Recommended Videos]
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Timeline

• 00:00 What is an SP Model (System Prompt Model)? A new concept that acts as a virtual OS

• 01:03 Controlling the issue of AI behavior fluctuating with every update from the user side

• 02:08 Lightning Leo reduces 4 minutes and 6 seconds to 0 seconds while preventing hallucinations

• 02:51 The Strawberry problem and the mechanism of Chain of Thought (CoT)

• 03:56 The problem of over-reasoning that splits the technical term "contempt"

• 05:00 Verifying the accuracy rate of "strawberry" and "contempt" with older models like GPT-4.1

• 06:04 Comparative experiment on questions that cannot be answered without specific knowledge

• 06:58 Hallucinations occur when asking about "Keito" without searching in GPT-5.0 Thinking

• 08:02 Why adding step-by-step instructions increases accuracy

• 08:54 The role of the SP model is to cut excessive reasoning and suppress hallucinations

• 09:59 Lightning Leo is versatile, featuring knowledge installation, router functions, high speed, and low noise

• 11:03 Explaining the three-layer structure: Core SP, Personal SP, and Task Bot

• 12:08 Core SP is designed to automatically select the optimal thinking route without the user being aware

• 13:14 Designing personality, character, and style with the middle-layer Personal SP

• 14:19 Once the core is designed, personality and task aptitude are automatically determined

• 15:24 Why prompt engineering will continue to be necessary

• 16:28 The promotional video for Lightning Leo was also created by Lightning Leo

• 17:33 Overcoming the excessive caution of the GPT-5 generation and maximizing the spontaneity of thought

• 18:36 Proposing a new profession: SP Model Designer

• 19:41 The need for the ability to counsel both AI and humans

• 20:48 CoreSP is a trade secret, Personal SP is for customization, and tasks remain as usual

• 21:51 Division of roles between SP model designers and prompt engineers

• 22:36 Demonstration of verifying "Kataimibito propaganda" using GPT-5.2 thinking

• 23:43 Lightning Leo correctly recognizes and answers about "Kataimibito"

• 24:49 It can provide correct answers because it captures and installs knowledge as chunks

• 25:53 4 minutes and 6 seconds when searching, 0 seconds time-saving effect with Lightning Leo

• 26:57 Solving the dilemma of it being slow when you make it think, and it being wrong when you don't

• 28:04 Real-world example of creating a storyboard for a PV using meta-prompting

• 29:19 Generating video on Grok using prompts created by Lightning Leo

• 30:24 Illustration of Kira Leo-chan also generated with Lightning Leo

• 31:30 Design philosophy of building the CoreSP first and then loading task prompts

• 32:33 Management is not perfect, so adjustments by the user are necessary

• 33:37 Announcement regarding the scheduled release of Kira-Leo in mid-February
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