This as-told-to essay is based on a conversation with Yesim Saydan, a branding and communication expert in her early 50s, based in the Netherlands. The following has been edited for length and clarity.
When I’m stuck on a business decision or need to come up with a creative idea or strategy, brainstorming usually starts with my Steve Jobs custom GPT.
My solo-consultancy business helps senior executives and established entrepreneurs grow their authority and influence through social media and brand strategies. But scaling that work on my own was challenging.
Before AI, if I wanted to scale the number of clients I could take on, my main option was hiring freelancers for special projects or tasks. I spent a lot of unnecessary time training the freelancers on my specific framework, and it often felt like they didn’t care as much as I did.
When OpenAI launched custom GPTs, everything changed. I used the feature to create over 17 custom GPTs to build my team. Then I thought of my ideal mentors and created custom GPTs of them.
I had to create more than 4 custom GPTs to get good results
One of my first jobs was at Citibank as a project manager on Wall Street, following my move to the US from Turkey for my MBA. That kicked off my 14-year corporate career, during which I worked in New York, Paris, and the Netherlands.
I started my business about a decade ago because I wanted more flexibility in my work schedule. At the time, social media was just starting to take off, and I saw a clear opportunity.
I’d played with AI tools before, but OpenAI’s custom GPTs changed the game. Initially, I envisioned creating my ideal four-person team of agents. I quickly realized AI produces subpar results when it’s overloaded with too many tasks.
Instead, I created a custom GPT for each important task I wanted the AI to perform. That’s how I ended up with more than 17 custom GPTs making up what feels like my perfect team.
I trained my AI team to allow me to focus on the bigger strategy
I can create a custom GPT in five or 10 minutes, but what actually makes it powerful is the training process. I create standard operating procedure documents for each task and client, serving as training materials for my agents that outline my methodology and frameworks.
I have client-specific AI agents trained on each major client’s tone, goals, and past conversations, so I’m never starting from scratch with a task. The training is ongoing. Every time I make a query or upload a document, the agent improves, just like a real employee would.
When I need to communicate or create content in a client’s tone of voice, the draft I end up with is so tailored that it feels like I spent hours perfecting it, when in fact my AI team handled it.
I’ve trained a market researcher, a sales call analyst, a proposal writer, a video scriptwriter, and even a custom GPT to evaluate LinkedIn profiles using six pillars to determine if the current LinkedIn presence builds authority, attracts their ideal client, and establishes trust, clarity, and uniqueness. These free me up to focus on big-picture strategy.
I taught my custom GPT to think like Steve Jobs and mentor me
After creating my ideal employees, I asked myself which mentors I would love to have alive or dead. Steve Jobs is known for his creativity and innovation, and there are numerous videos already online about him; he’s the perfect mentor to create a custom GPT for.
In the instructions, I started with things like, “you are Steve Jobs, you have decades of experience in X, Y, Z, your most important skill is creativity, innovation, thinking out of the box.”
There are two types of video transcripts I trained it on. I uploaded transcripts from videos where he explains his strategies and what he looks for in products. The second approach was training through examples. I found videos showing how he launched products like the iPhone or iPad, so the AI learns from both his thought process and his execution of those launches.
To get it to the level it is now, I spent roughly 40 hours researching and building training assets, including PDFs and other materials the GPT can use as references. I continue adding more whenever I find relevant material, and I now have custom GPTs for Dan Kennedy and Elon Musk as well.
I have to avoid asking certain questions to get the most appropriate responses
The frustrating part with training AI models is that I can give it a lot of information that’s required to have the superpowers of Steve Jobs, but then the AI could take that and produce a lot of different things.
When I prompt it, I avoid asking questions like “What do you think of this idea?” because the AI usually wants to agree with me and please me. Instead, I ask, “On a scale from one to 10, how good is this idea?”
It’s not going to say the idea is bad, but now it might tell me it’s a five. Then I’ll ask, “OK, what would make it a 10?”
That’s usually when it starts drawing on the experience of Steve Jobs that I’ve trained it with. We can go back and forth until I get the most useful and honest feedback possible.
It depends on the task. For more strategic outputs, I usually go through three to five rounds of refinement.
Read more from our Tiny Teams series
AI scares me, but there’s no turning back now
When a product like NotebookLM was introduced, I started thinking, “Oh my God, this is going to make the entire human race obsolete.” I find AI products fascinating at first, but they can really scare me.
I truly believe we don’t know what the world will look like even a year from now. Sometimes I literally freeze thinking about the impacts, and if everyone will end up homeless, but I usually try to remind myself I’m not God or a higher power, and I don’t know what will happen. This calms me down.
I also realized that AI, by itself, is powerful, but what makes it truly magical is when we combine our expertise and skills with it. Using custom agents as an extension of our brain, rather than a replacement, is what really produces great output.
There’s no turning back from it.
Read the full article here
