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AI Does Not Think — A Methodology for Designing AI Collaboration

AI tools and models continue to change. Rather than memorizing how to use specific tools or the “correct” prompts, this course covers methodologies for making judgments, verifying results, and designing workflows for effective AI collaboration that remain reliable even in a changing environment. Through real screens and examples, you will learn the process of defining problems, designing context, critically evaluating and verifying AI-generated results, and making final decisions instead of simply adopting them as-is. You will understand why outcomes vary from person to person across different tasks such as AI coding, planning, and design, and create your own reproducible workflow.

(3.0) 2 reviews

27 learners

Level Beginner

Course period Unlimited

AI
AI
AX(Agent Experience)
AX(Agent Experience)
AI
AI
AX(Agent Experience)
AX(Agent Experience)

What you will gain after the course

  • Principles and decision criteria for AI collaboration that apply regardless of the tools used

  • A Critical Review and Validation Framework for Adopting, Modifying, or Discarding AI Results

  • Personal workflows applied to AI coding, planning, and design

AI Adoption (AX), AX (Agent Experience): a course for setting the standard
Learn how to use AI not as an automation tool,
but as a partner that supports your thinking.


AI: Don’t overuse it

Don't just become someone who uses AI well;
become someone who can control AI.


If you have ever used an AI’s answer as-is
only to discover unexpected errors later,
and ultimately had the responsibility for the result fall on you,
you will immediately understand why the principles covered in this course are necessary.

If you have been letting time pass with decisions and responsibility blurred because you could not distinguish between tasks you can delegate to AI
and moments when people must make the judgment themselves,
with both decisions and responsibility left ambiguous,
then this course’s guidelines
can serve as a clear turning point.

If following complex tools and features to learn how to use AI
has only made the criteria for when to use AI and when to stop
even more unclear
this course can help sort through that burden.

Try using AI now
not as something that thinks for you,
but as a colleague that helps organize your thoughts.
Your workflow will become simpler,
and your decisions may actually become clearer.trong khi khả năng phán đoán lại có thể trở nên rõ ràng hơn.


Learn when and how to use AI
and develop criteria for verifying results instead of blindly trusting them
within your workflow.


Rather than letting AI think for you,
learn how to use it as a partner that organizes your thoughts and assists with your work.




By the end of this course, you will be able to

Establish clear guidelines for using AI and use it with confidence

Whenever you use AI,
if you have ever wasted time wondering, “Is this result correct?”
or “Could there be a better prompt?”

this course will give you clear criteria for making decisions.

Rather than blindly trusting AI’s answers,
you will learn how to judge for yourself how much to rely on them and what to filter out.
As a result, without blindly relying on AI,
you will develop the ability to selectively use only what you need.


Turn AI into a practical partner without complex tools

If you have been overwhelmed by countless AI tools and complex prompt-writing techniques,
this course offers a different option.

Instead of adding more tools,
this course covers how to create a structure that supports your work
without overusing AI.
You will learn practical guidelines for using AI not as an “all-purpose problem solver,”
but as a companion that helps organize your thoughts and work.


Design your own optimal workflow with AI

You will develop clear criteria for deciding when to use AI and
when it is better not to use it.

You can distinguish moments when getting help from AI
actually creates inefficiency,
and instead of using AI’s answers as they are,
you can consistently improve the quality of your work
through a minimal routine of reviewing and refining them.


Develop standards for trusting and using AI-generated outputs

Instead of blindly accepting information generated by AI,
you will learn how to frame your questions and
what criteria to use for verification.

Through this,
you will maximize the strengths of AI and control its weaknesses,
and across various areas such as work, learning, and content creation,
develop the standards to use AI with confidence and without anxiety.






✔️

Using AI means learning how to judge, not what to believe.

AI does not think.
It does not decide.
It does not take responsibility.
—those roles still belong to humans.

Step 1. Define the Problem (People)

Defining what to solve is the person's role.
Before using AI, clarify the problem first.

Step 2. AI Input (Tool)

AI suggests drafts and options.
Its role is not to ‘make decisions for you,’ but to provide input.

Step 3. Human Review (Judgment)

People decide what to adopt, revise, or discard.
The final judgment and responsibility are fixed here.

The Most Common Mistakes People Make When Using AI Every Day
Without standards, AI becomes a ruler rather than a tool


📚

The moment your criteria disappear,
AI becomes a master, not a tool.

Section 1

Introduction to Minimalist AI Architecture

Instead of complex tools and massive systems,
we propose an approach to using AI that keeps human thinking clear.

In this course,
we explain the core concepts of a
minimalist AI architecture that minimizes cognitive load
, centered around a “constitution” that establishes standards for AI use and
a “Socratic guide” that leads your thinking through questions.



Section 2

A Perspective on Using AI: The Philosophy of Simplicity

The more you used AI, the easier you expected your work to become, but
if it instead became more complicated and exhausting,
this course offers a different perspective.

Instead of adding more tools,
this course covers how to turn AI into a genuinely helpful partner
by reducing unnecessary elements
so that human thinking remains clear.


Section 3

Criteria for Deciding When to Use AI

We cover how to decide whether to use AI
based on decision criteria rather than
habit or atmosphere.

By distinguishing when AI is helpful
from when it is better not to intervene,
this provides criteria for keeping AI
as a controllable tool rather than an obstacle to thinking.



Section 4

Complex prompts do not solve problems

The more unstable AI’s answers become,
the more likely we are to make the prompts more complex.
However, most problems
occur not because the prompt is insufficient,
but because the question has not been properly organized.

In this course,
we explore why
short, clear questions
produce more controllable results than long, complex instructions.


Section 5

Strategies for Validating and Using AI Responses

Rather than AI’s answers,
this section covers how to keep human judgment at the center.

Learn a mindset of critically reviewing AI-generated results
rather than using them
as-is, and adjusting them
to align with human standards.



Section 6

Designing Custom AI Workflows

This course covers
workflows that position AI not at the center of automation, but in a role that supports human thinking
.

By clearly defining in documentation and workflows how far AI should be involved
and where it should stop,
you will design a structure that lets you use AI every day
without losing control over your judgment.


Section 7

The key to keeping AI as a tool

The conclusion of this course is simple.
AI does not think,
make decisions, or
take responsibility.

Therefore, more important than knowing how to use AI well is
clearly understanding the boundaries AI must not cross.
In this section,
we organize the criteria you can use to maintain those boundaries yourself.



If you have these concerns, this course can help

📌

Professionals who feel overwhelmed by using AI

If you’ve tried various AI tools but can’t figure out how to apply them to your actual work
and feel uneasy using the results as-is, so you spend time revising them every time

📌

If you're using AI but aren't sure you're using it well

If you have tried various AI tools but
aren’t sure whether you’re using them effectively at work,
don’t trust the AI-generated results,
and end up revising them manually anyway,
this course provides clear guidelines.

📌

If AI seems promising but you’re at a loss as to how to use it

If you recognize AI’s potential but
hesitate to adopt it because you lack clear standards for what structure to use and how far to leverage it

this course provides clear direction.




Things to know before enrolling

Practice environment

  • No special tools need to be installed.

  • All you need is a PC with an internet connection.

  • Having a notebook to organize your thoughts and standards will help with learning.

Prerequisite Knowledge and Notes

  • This course is suitable for those who have experience using AI tools but
    aren’t sure how much they can trust the results.

  • If you're uneasy about using AI outputs as-is
    and have been spending time verifying and revising them every time, this course will help.

  • Recommended for those who want to apply AI to their work or daily life
    but have been hesitant because they lack clear guidelines.

Learning materials

  • Materials related to the minimalist AI architecture explained in the course are provided.

  • We also cover documents that help organize standards and ways of thinking for using AI.

  • You can gain practical guidelines for applying AI
    to fit your personal workflow.


📍 Notice

This course is a video course based on unedited screen recordings and will be updated sequentially. Rather than simply following the features of specific AI tools, it covers, along with actual work screens, methodologies for defining problems, verifying results, and making final decisions when collaborating with AI.

Recommended for
these people

Who is this course right for?

  • People who use AI but have no standards for how much they should trust the results or take responsibility for them

  • People who want to understand why AI coding results differ from person to person and how to design those differences.

  • People who want to develop enduring AI collaboration methodologies instead of chasing tool and model trends

Need to know before starting?

  • All you need is a PC or Mac and an internet connection. No development experience is required.

  • Specific paid AI tools are not required. The examples in the class can be adapted as the tools evolve.

Career Verified

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Courses

At the early-stage startup I was previously part of, I learned more than just how to write code; I learned the structure of how technology functions as a service.

Although my primary focus was on web frontend development, I took responsibility for the core service paths by designing backends and data flows whenever necessary. In particular, I built and operated a pipeline to stably collect, refine, and manage over 1 million fashion product data points using FTP/SFTP and web-based architectures.

Through this experience, I have become convinced that what matters more than any specific language or framework is the ability to understand the overall system flow and responsibility structure.

Currently, I am designing AI-based systems in web environments, focusing on defining structures and control models before execution. Rather than simply adding features, my work is closer to designing state transitions and validation flows.

Starting as a non-major and getting to this point through self-study, I am well aware of the roadblocks and realistic constraints. That is why in my lectures, I focus on "why we design this way" and "how to make decisions" rather than showing off technical skills.

A structure that leaves only the essentials,
instead of increasing complexity.

That is the development philosophy I strive for.

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24 lectures ∙ (36min)

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