u/Dramatic_Desk_7626

Simple test: open ChatGPT and ask “best [your industry] tools/services.”
Are you in the answer?
Most businesses I’ve spoken to have never checked. They invest in SEO, content, ads but have zero visibility into whether AI is sending them customers or ignoring them completely.
Google has Search Console. There’s no equivalent for ChatGPT or Perplexity.
Is this a problem you’ve experienced? Would you pay for a tool that tracked this automatically?

For years I tried to be more disciplined. Wake up earlier. Work longer. Push through resistance.
It worked sometimes. Mostly it didn’t.
Then I tried something different: instead of fighting my laziness, I designed around it.
Here’s what that looked like practically:
If a task takes more than 2 minutes to start, I won’t do it. So I made every task start in under 2 minutes.
If I have to think about what to do next, I’ll scroll instead. So I wrote my next 3 tasks the night before.
If the environment isn’t set up, I’ll find an excuse. So I leave my laptop open on the right file before I go to sleep.
The best system isn’t the one that requires the most willpower. It’s the one that works when you have none.
What’s one thing you designed around your own laziness that actually worked?

For the first two weeks I was building like most people do:
“Add a dashboard”
“Now add a chart”
“Now add filters”
Everything worked. But nothing fit together. I kept rebuilding the same sections because the overall structure wasn’t clear — to me or to Lovable.
Then I tried something different. Before touching Lovable, I wrote a full PRD — product requirements document. Nothing fancy. Just:
— What problem does this solve
— Who is the user
— What are the core features (prioritized)
— What does each screen do
— What are the user flows
Then I pasted the entire PRD as the first message in a new Lovable project.
The difference was immediate.
Lovable started making decisions that were consistent with each other. The navigation made sense. The data model fit the features. I stopped rebuilding things I’d already built.
The prompts after that were small and specific — “build feature 3 from the PRD” — instead of explaining context every single time.
Three things I learned:

1. Lovable is only as good as the context you give it. A PRD is context.
2. The PRD doesn’t need to be perfect — it needs to be clear about priorities
3. You will change the PRD as you build. That’s fine. Update it and re-paste.
   If you’re rebuilding the same feature more than once, you probably don’t have a Lovable problem — you have a clarity problem.
   Anyone else using PRDs or similar docs before starting a build?

4 weeks ago I had an idea. Today it’s live with real users.
Here’s what actually happened in between the parts nobody posts about:
Week 1: Everything looked perfect in Lovable. The UI was clean, the flows made sense. Then I connected Supabase and realized I had no idea how auth actually worked.
Week 2: Built the core feature. Worked perfectly in dev. Broke completely in production. Spent 3 days on a bug that was one missing environment variable.
Week 3: Added Stripe. Thought I’d finish in a day. Took a week. Not because Lovable was hard because payments touch everything.
Week 4: Launched. First user signed up within 48 hours.
What Lovable is genuinely good at: moving fast on UI, iterating without breaking things, letting non-technical founders build real products.
What it doesn’t do for you: thinking through your data model before you build, handling edge cases in complex API integrations, making product decisions.
The honest take: if you treat Lovable as a tool that executes your thinking, it’s incredible. If you treat it as a replacement for thinking, you’ll rebuild the same thing 4 times.
What’s the biggest non-obvious lesson you learned building your first Lovable project?

Something I didn’t expect when I started tracking AI visibility separately for different models:
ChatGPT and Perplexity don’t just give different answers they pull from fundamentally different source layers.
ChatGPT leans heavily on:
— Wikipedia
— G2, Capterra, review sites
— Editorial publications
— Established industry blogs
Perplexity leans on:
— Reddit and community forums
— Recent discussions and UGC
— News and time-sensitive content
What this means practically:
A brand that invested in Wikipedia presence and G2 reviews will show up consistently in ChatGPT — and barely exist in Perplexity.
A brand with strong Reddit and community presence will dominate Perplexity and be invisible in ChatGPT.
The mistake most agencies are making: running the same optimization strategy for both models.
If your client is only visible in one it’s probably a platform mismatch, not a content problem.
The fix isn’t to do more. It’s to track them separately, understand where the gap is, and target the right source layer for each model.
Has anyone mapped this split for their clients? Curious what patterns others are seeing.

\*Is anyone else tracking AI visibility separately from traditional SEO?\*

\*I've noticed my clients rank well on Google but barely get mentioned when someone asks ChatGPT the same question. Started monitoring this manually and the gap is significant.\*

\*How are you handling AI search visibility for your clients? Any tools or processes you've built?\*