Most people start out using AI like a chatbot.
They ask a question, get an answer, and move on. That works for simple tasks. But once AI becomes part of your actual work, the limitations show up quickly.
You end up reusing the same prompts. Context gets buried across different chats. And when you return to a piece of work later, the AI often has no real sense of where you are or what matters now.
That is the point where the issue is no longer prompting. It is structure. And in ChatGPT, two of the most useful structural options are Custom GPTs and Projects.
What most people misunderstand
At first glance, Custom GPTs and Projects look similar. They both sit in the ChatGPT interface. They both allow you to add instructions. They both can work with uploaded files.
So people often assume they are interchangeable.
They are not.
The simple way to think about this is:
- Custom GPTs are tools.
- Projects are workspaces.
That distinction matters because they solve different problems.
If you choose the wrong structure, you usually create friction. Outputs become inconsistent. Context gets messy. And the AI starts feeling less useful than it should.
Custom GPTs are best for repeatable work
A Custom GPT works well when you want a similar kind of output every time.
In the video, the example is a proposal generator. The GPT is set up with a clear title, description, instructions, and a small set of supporting knowledge files. Those files include things like a sample client brief, a standard proposal structure, and a tone of voice guide.
That setup gives you consistency.
You can give the GPT a new input, and it will still aim to produce the output in the same structure, with the same tone, and with the same underlying logic.
This is what Custom GPTs are good at. They act more like specialised tools than open-ended workspaces.
Good use cases for a Custom GPT
- Proposal generation
- Structured research outputs
- Transcript processing with a defined output format
- Business idea development within a fixed framework
- Brand-aligned communications
The key idea is that the instructions and knowledge should be relatively stable. You are refining the tool so it behaves reliably over time.
Projects are best for evolving work
Projects are different.
They are better suited to work that changes over time, where the context needs to build and where the source material may be updated as the work progresses.
That might include:
- Client engagements
- Research projects
- Internal documentation
- Business planning
- Book writing
In a Project, chats are grouped together. That matters because the work has continuity. You are not just asking for a one-off output. You are building context over time.
You can also add files, but in this case the files are part of a living knowledge base. You may add stakeholder interviews, current workflows, internal notes, drafts, or updated documents as the project evolves.
That makes Projects more conversational and more flexible.
But it also means they need more thought. Flexibility is useful, but it comes with the risk of clutter if you do not manage the workspace properly.
The practical model: tools, workspaces, systems
One of the clearest ideas in the video is that the real value comes from combining both.
Custom GPTs are your tools.
Projects are your workspaces.
The workflow that connects them is your system.
This is the bigger shift.
Most people are still focused on prompts. But the real leverage comes from designing the environment AI works inside.
That means thinking beyond isolated interactions and asking better structural questions:
- What kind of work repeats often enough to justify a tool?
- What kind of work evolves over time and needs a workspace?
- How should those two interact inside the business?
For example, you might build a proposal generator as a Custom GPT so proposals are created quickly and consistently. But each client engagement could sit inside its own Project, where research, context, and evolving materials are stored and worked through.
That is a much stronger setup than trying to force everything into one place.
Common mistakes and trade-offs
There are three common mistakes highlighted in the video, and each one points to a structural misunderstanding.
Using a Custom GPT as a prompt wrapper
If a Custom GPT is just a long prompt pasted into a shell, the output often remains unreliable. A well-structured GPT should produce a consistent result with minimal clean-up. If it does not, the design probably has not been thought through properly.
Using Projects as file dumping grounds
Just because you can upload many files does not mean you should. Too many loosely related files create noise. The AI has less clarity on what matters, which usually lowers output quality.
A better approach is to hand-pick the most relevant files and make the project instructions explicit about why those files are there and how they should be used.
Using a Custom GPT as a live database
If you are constantly swapping files, changing the knowledge, and rewriting the instructions, that usually means the work is evolving too much for a Custom GPT. In that case, it probably belongs in a Project instead.
The trade-off is straightforward:
- Custom GPTs give you consistency, but less fluidity.
- Projects give you flexibility, but require more discipline.
Neither is automatically better. The right choice depends on the kind of work you are doing.
Why this matters for expert-led businesses
This matters because expert-led businesses do not just need more output. They need better operational design.
If your business depends on judgement, context, and credibility, then AI needs to be embedded carefully. It cannot just sit on the side as a generic chatbot.
Used well, AI becomes part of the operating environment.
It helps you create repeatable tools for repeatable tasks. It helps you manage evolving work with better context. And over time, it supports a more deliberate system around how expertise is delivered.
That is a much stronger use of AI than simply asking better one-off questions.
The real story here is not that AI is getting smarter. It is that the businesses who structure their environment properly will get more value from the same underlying tools.
Key takeaways
- Use Custom GPTs for repeatable tasks that need consistent outputs.
- Use Projects for work that evolves over time and needs continuity.
- Do not confuse stable tools with living workspaces.
- The best setup usually combines both.
- The real leverage comes from system design, not prompting alone.
If you are building an expert-led business, that is the more useful way to think about AI.
Not as a chatbot.
As infrastructure for how work gets done.
If you want to go deeper on this, Expert OS is where we help serious experts integrate AI into the structure of their business in a way that is practical, calm, and built to last.
