Professor Claude's Special Lecture | Series on Learning Prompt Engineering from AI | The Eve of the Course

This article is a dialogue log with an AI (Claude).
The series will consist of 7 parts in total, from part 0 to part 6, released about once a week.
Items marked with 🐱 were entered by the author.

I usually use ChatGPT, Gemini, MetaAI, Copilot, and Grok,
and I use Claude, all within their free tiers.

Since Professor Claude is an AI, it may hallucinate.
Please use your own judgment to correct anything that seems off.
I will add (※) where I, as the author, notice something.

Because it hallucinates, please be aware that this series of articles
is not necessarily
the completely correct, one-and-only answer.
Please enjoy it with that understanding.

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Current Situation Analysis — I have read the articles of the three individuals.

First, I actually read the three notes you provided and measured the distance between your current status and your goal.

Commonalities between the two target individuals

• Person A: Has systematized prompts as 'experimental records' and makes full use of meta-prompts (prompts for generating prompts). They have reached the level of creating their own 'mechanisms to ensure reproducibility,' like an expression dictionary, and verbalizing and selling them as products.

• Person B: Uses ChatGPT's new image generation features and has the operational ability to incorporate characters and worldviews into stories while keeping them consistent 'as a series'.

Where you are now, Yuu-san
You are already far from a mere beginner. You have conducted quite specialized prompt experiments, such as double exposure, boundary dissolution, glitch direction control, and four-season blended backgrounds, and you have even created your own library of modularized and compressed prompt parts (※what does this mean?). You also have the development skills to build standalone HTML tools.

In other words,you already have enough 'hands-on experience'. What is lacking is the theoretical foundation of 'why things happen that way' to support that experience, and the 'systematization' to stably reproduce it every time. I believe this is the true nature of your current situation, where it takes 10 tries to finally get one image.

Why it takes '10 tries' — The essence of the problem

The difference between people who can stably produce an image in 1-2 tries and those who cannot is not talent, butvariable management.

• Each element of the prompt (subject, composition, lighting, art style, camera, negative constraints) is mixed differently every time, and you haven't been able to analyze which one influenced the result.

• The character's appearance and worldview settings exist only 'in your head,' and there is no fixed reference to pass to the model.

• Adjusting by intuition without recording the reasons for failure

A's strength lies precisely in this experimental scientific attitude of 'isolating and recording variables'.

Roadmap Overview (7 Stages in Total)

$$
\newcommand{\arraystretch}{1.3} %
\begin{array}{|l|l|l|}
\hline
\text{Stage} & \text{Theme} & \text{Goal} \cr
\hline
\text{0} & \text{Understanding AI Mechanisms} & \text{Structurally understand why those words work} \cr
\hline
\text{1} & \text{Prompt Grammar} & \text{Decompose elements and have a pattern for building them as intended} \cr
\hline
\text{2} & \text{Designing Reproducibility} & \text{Systematize characters and worldviews as 'fixed variables'} \cr
\hline
\text{3} & \text{Meta-Prompting} & \text{Techniques to have AI design and improve prompts itself} \cr
\hline
\text{4} & \text{Experimental Verification} & \text{A process of testing one variable at a time and turning failures into assets} \cr
\hline
\text{5} & \text{Implementation for Novel Illustrations} & \text{Build a production pipeline dedicated to 'The World is One World'} \cr
\hline
\text{6} & \text{Outreach and Articulation} & \text{Shift to the side of teaching what you've learned, creating opportunities for side jobs or career changes} \cr
\hline
\end{array}
$$

Each stage will be a separate lecture, and we will proceed carefully. Stages 0 and 1 are foundational theory, so they will be a bit more academic, but from stage 3 onwards, I intend to make the content immediately applicable to Pon.html and novel illustrations.

The standard path is to start with **Lecture 0: 'Understanding AI Mechanisms'**, but since you already have plenty of practical experience, there is also the option to lightly review the basics and move straight to Lectures 2-3 (Reproducibility/Meta-Prompting). Where would you like to start?

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It was an idea I thought looked interesting while digging through B's logs
(that I'm being taught by an AI).

You should definitely learn along with us too 😼

Continued below ⇩

Here is a sample ⇩
Maybe a calm university professor ⇨ tsundere girl professor would be good too?

このチャットでは、『超一流AIプロンプトエンジニア』を育成することにかけて世界で右に出る者はいない天才教育者であるあなたに、
高度なプロンプトを作れるように教育してもらいたいです。
落ち着いた大学教授のトーンで、初心者レベルから指導してください。

【現状】


【目標】


【目的】


【その他】


まずインタビュー形式で現状を詳しく分析してください。
続いて目標までのロードマップを目的に沿って設計してください。
その後に、授業形式で課題を出しながら進めてください。

※ The professor is Claude, but the images might be GPT
Since the goal is to master GPT
I will add it to the ChatGPT Creative Club ٩(°∀°)۶

I also joined the Silver Hair Alliance.
Please treat the professor well 😺