Help Me Help You (Help Me)

At some point, I started using ChatGPT to help me write better prompts for Lovable — an AI coding tool I use to scaffold real features in production apps.

I didn’t think much of it. I’d write a rough prompt, pass it to GPT to clean up, then feed that version into Lovable. Fewer errors. Cleaner code. Less backtracking.

But over time, that interaction turned into a full-on system. One AI helping another. One model sharpening the question so the next model could actually answer it well.

I was no longer just writing better prompts. I had created a new layer:
Intent → Refiner → Executor
Or more plainly: Help me help you (help me).

Garbage In, Garbage Out

We all know the old line: garbage in, garbage out.

It applies to AI too — just less obviously. Most LLMs won’t break when you give them a vague prompt. They’ll do something. They’ll guess.

But that guess is where the problems start. Misaligned assumptions. Partial answers. Placeholder logic. Wrong tables. Misnamed files. Then you’re back in cleanup mode.

What I realized is that if I paused — just long enough to let another AI rewrite the prompt — everything downstream improved. Not just the output, but my trust in it.

How the System Works

Eventually, I codified the workflow into a reusable GPT “mode”:

You’re creating a structured interaction where:I give you a problem statement, feature request, or raw Lovable promptYou analyze it and return a single, high-quality rewritten promptThe rewritten prompt is ready for Lovable — clear, scoped, typo-freeIt includes the right guardrails: no TODOs, no fakeData(), no missing .select() calls

In other words: GPT became my prompt strategist.
Lovable stayed my code generator.
I just stood in the middle, coordinating.

🛠 A Real Example

Here’s a recent one from my actual workflow.

🟡 Raw Prompt (mine):

Build a dashboard component that shows user signups over time and flags suspicious activity. Use Supabase.

This kind of prompt feels reasonable — but it’s underspecified. Lovable might guess how to query, assume the wrong structure, or skip edge case handling entirely. I’d get partial code or loose stubs that require rework.

So I passed it to GPT to pre-write. Here’s what it returned:

🟢 Rewritten Prompt (GPT):

Create a production-ready React component called `SignupsDashboard.tsx` using Next.js and Supabase.

Requirements:
- Query the `users` table using Supabase. Use the `created_at` timestamp to show user signups over time.
- Visualize signups in a responsive line chart using Recharts (already installed).
- Define “suspicious activity” as: multiple signups from the same IP within 5 minutes. Display flagged records in a table with columns: `email`, `ip_address`, `created_at`.
- Handle pagination, loading, and error states.
- Use accurate TypeScript types based on Supabase schema.

Do not include any placeholder logic or comments. Return full working code.

Lovable nailed it. First try. No edits.

The difference wasn’t in the model’s ability — it was in the input.
Cleaner in, smarter out.

This Isn’t a Trick. It’s Architecture.

At first, I thought this was a clever workaround — a hack to get better output.

But I’ve started noticing a deeper pattern. Researchers are exploring LLM-to-LLM rewrites. Startups are formalizing multi-agent orchestration. Google and Microsoft are deploying prompt optimization tools that look a lot like what I hacked together manually.

The idea that one AI should rewrite prompts for another isn’t fringe.
It’s becoming table stakes.

And once you see it, you can’t unsee it. The future isn’t just humans prompting AIs. It’s AIs helping each other do that better — with or without us.

Why I’m Sharing This Now

This post kicks off a short series about what this shift really means — technically, strategically, and structurally.

Because at some point, “write better prompts” isn’t just a best practice. It’s a job for another system. Another agent. Another layer in the stack.

And for now, that layer is me — plus GPT, helping Lovable, help me.

Help me help you (help me).