Build a Custom GPT with your GPT architect
The Actual Process I Use:
There’s a big difference between:
“Making a GPT for fun”
and
Designing a standalone GPT that improves your decision-making, workflows and makes commercial sense. Everything that takes time has an opportunity cost, but following these steps has yielded great results so far.
I’ve used my GPT Architect to build GPTs for presentation builders, product launches, house renovation, branding documents and much more. If you haven’t build one then do that first - takes two minutes - you can find details here.
Here’s the process I follow to use my GPT Architect to build more GPTs and the reasoning behind each step.
Step 1: Decide If You Need A GPT
Before building anything, ask:
Do I operate inside a defined niche?
Do I make similar strategic decisions repeatedly?
Do I already have a repeatable way of thinking?
Am I trying to scale judgment, not just output?
Am I trying to create a role, not just complete a task?
If the answer is no - don’t build it.
A standalone GPT is useful when it codifies a pattern. Not when you’re still exploring. The best thing to do is ask your GPT Architect if this is a good use case for a GPT - quite often it says no! Provide the architect with all the information you intend to upload and the context to work with it and map out the exact use case before you start building it.
Step 2: Define the Economic Outcome
Don’t start with features.
Start with outcomes.
Ask:
What business result should this GPT help me dial-in?
Examples:
Improve sales conversion decisions
Structure client delivery
Refine positioning
Stress test strategy
Standardise hiring criteria
Challenge weak assumptions to improve decision-making
If it doesn’t tie to money, efficiency, or leverage - it’s probably unnecessary.
Step 3: Define the User (Even If It’s Just You)
Who is this GPT for?
Founder?
Sales lead?
Ops manager?
Junior team member?
Skill level matters.
Context matters.
Decision authority matters.
A GPT for a beginner and a GPT for an experienced operator should not behave the same way.
Step 4: Anchor It to a Framework
This is where most people fail.
If you don’t define:
Phases
Decision criteria
Evaluation structure
Core principles
You’ll get generic outputs.
Your GPT needs a “spine.”
That spine can be:
A methodology
A diagnostic model
A checklist system
A phased transformation
A scoring matrix
Without that, it defaults to chat-level advice. Ensuring information that’s matched to your ultimate objectives is a critical time-saver. I have GPTs that provide code snippets, some that use traffic-light systems to determine decision-gating, and others that provide presentation-ready slides.
Step 5: Install Guardrails
Be explicit about:
What tone it should use
What it should avoid
How structured outputs must be
Whether it should challenge you
What domain it must stay inside
Most people under-specify here. I always make sure I’m getting very concise, no-frills advice. No sugar-coating or bloated details.
The difference between average and elite output is usually constraint clarity.
Step 6: Define Output Structure
Don’t leave formatting to chance.
For example:
Situation analysis
Leverage points
Risks
Recommendation
Structure improves thinking. And thinking is what you’re scaling. You need to avoid bloated documents, and get the right level of information and actionable insights.
When You Should Build a Standalone GPT
✔ You have repeatable decision patterns
✔ You want intellectual leverage
✔ You’re building IP
✔ You want consistency in reasoning
✔ You operate inside constraints
High-leverage use cases:
Founder Operating System
Offer Stress Tester
Funnel or Product Auditor
Sales Narrative Builder
Hiring Evaluation Framework
Strategy Challenger
When You Shouldn’t
✘ You’re still figuring out your niche
✘ You don’t have a framework yet
✘ You just want “better ChatGPT answers”
✘ You expect it to replace thinking
✘ You won’t use it consistently
A GPT doesn’t create clarity.
It amplifies the clarity you already have.
What Information to Provide When Building One
If you’re working with someone to architect it, provide:
The outcome it exists to serve
The context it operates in
The user profile
Your decision-making framework
Constraints and exclusions
The tone and standards required
How it will be used weekly
Vague input → generic system.
Sharp input → high-performance operator.
How to Actually Use It Once It’s Built
Most people build one and then stop using it.
Better approach:
Use it to stress test ideas
Ask it to challenge your assumptions
Run diagnostics before making decisions
Extract repeatable frameworks from outputs
Refine it over time
Think of it as version 1 of your internal operating system. This is absolutely vital - if any of the results are not to your liking - then copy and paste some of the chat into your GPT Architect and tell it what’s wrong. Sometimes you may need to refine the GPT instructions, but other times you just need to ask it to do something differently in the chat.
Final Thought
A standalone GPT is not about automation.
It’s about codifying how you think -
so your thinking becomes consistent, scalable, and eventually productizable.
If you can’t articulate how you think yet, start there.
Then build the GPT.