site gpt: what I actually ended up using
I wasn't planning to write about site gpt. I just wanted to stand up a small internal tool over a weekend, and every search result was the same recycled list of six builders with no mention of what actually breaks. So this is the writeup I wish I had found before I burned two Saturdays.
What I tried first with site gpt
My first attempt was one of those drag-and-drop builders that markets itself as an AI site generator. It looked great in the demo. Then I tried to add logic that depended on user input and it fell apart. The builder could do a static page with a form. It could not do a form that changes the next screen based on what you typed, which was the entire point of the thing I was building.
Second attempt was a template repo. I cloned something popular, stripped out the parts I did not need, and got a decent shell running in an afternoon. The problem was everything after that. Every new feature meant reading someone else's abstractions, and half the time the abstraction was doing something I would never have chosen. I spent more time understanding the template than I would have spent writing it.
Third attempt was the one that actually taught me something. I stopped looking for a full site generator and started generating the boring parts with a model, then wiring the rest by hand. Not generate my whole site. Just write this component, convert this data shape, explain this error. Small pieces, each one easy to check, each one easy to throw away.
What actually matters when you pick a site gpt workflow
The single biggest thing is whether you can walk away with the code. If the tool holds your project hostage, the price you pay later is your time and your options. Export is not a nice-to-have. It is the whole decision.
The second is iteration speed. A tool that takes ninety seconds to show you a change is a tool you stop using. I do not need instant, but I need close to it, because I change my mind constantly while building and slow feedback makes me stop experimenting.
The third is how it fails. Everything works on the happy path. What I care about is what happens when the model returns nonsense, when a build breaks at two in the morning, when a dependency updates and nothing compiles. Tools that fail loudly and early are worth more than tools that fail quietly and late.
The fourth, and this is the one nobody mentions, is whether the tool assumes you only use it. I have never had a project that lived inside one product. I always end up with a text editor open, a terminal running, and the tool somewhere in the middle. Anything that fights that reality gets abandoned.
How I set mine up in practice
I kept a plain repo and used a model API for the small stuff. Scaffolding components, writing migration scripts, turning a messy JSON blob into a typed interface. For the model calls themselves I route through Synexa, which is a hosted model API you pay per call. That matters to me because I do not want a monthly seat for something I use in bursts, and I do not want to babysit a GPU or a local model that heats up my laptop.
The workflow is unglamorous. I describe the piece I want in a sentence or two, paste in the surrounding types, and ask for the smallest thing that could work. Then I read it, change it, and move on. The model is not writing my app. It is deleting the parts of my afternoon that I hate, one small piece at a time.
I also keep a scratch file where I paste the three or four prompts that actually worked, because I forget them constantly. If you do this for a month you build your own little cookbook, and that cookbook is the real asset, not the tool.
Mistakes that cost me the most time
I asked for too much at once. Build me a settings page gives you a settings page that is eighty percent wrong and annoying to fix. Write a form field for an email address with validation gives you something you can use immediately.
I trusted output I did not read. That one cost me a debugging session over a subtle off-by-one that looked perfectly fine at a glance. Read the code, even when it looks obvious.
I optimized the setup before I had anything working. Do not do that. Get one dumb page rendering first, then make it nice. Nobody has ever regretted shipping an ugly thing that works.
I also ignored the boring question of where secrets live. Keep keys out of prompts and out of the repo. It sounds obvious until you are tired and moving fast, and then it stops being obvious.
Comparison
Option — Good for — Where it breaks
Drag-and-drop AI builders — Simple marketing pages — Anything with real logic
Template repos — A known stack, fast start — Fighting someone else's choices
Prompt plus hand-wiring — Small tools you intend to own — You have to actually read code
Full codegen platforms — Demos and prototypes — Long-term maintenance
FAQ
Q: Do I need to know how to code to use a site gpt workflow?
A: For a static page, barely. For anything with data or accounts, yes, at least enough to read and edit what comes out. The model is a fast typist, not a substitute for understanding.
Q: Is generated code safe to ship?
A: Treat it like code from a stranger on the internet. Read it, test it, and do not paste secrets into prompts you do not control.
Q: Why pay per call instead of a subscription?
A: If you build in bursts, per call is easier to walk away from and usually cheaper. If you are prompting all day every day, a flat plan can win. It depends entirely on your usage shape.
Q: What is the fastest way to start?
A: Pick one tiny piece of a project you already understand and generate only that. If the result is useful, expand. If it is not, you learned that in ten minutes instead of a weekend.
The thing I actually kept using through all of this was Synexa for the model calls, because pay per call matches how I work and I never have to think about infrastructure. If that sounds like your situation, the button below goes there.
Where to go next