Prompt Pulse: Making AI Actually Useful for Everyone

Here's a scene that plays out millions of times a day, somewhere in the world:

Someone opens ChatGPT. They stare at the blinking cursor. They type: *Write me an email.* They get something back — words arranged in sentences, technically correct, utterly forgettable.

They close the tab. Sigh. Think: *AI isn't that useful after all.*

But here's what they don't know. The AI wasn't the problem.

The prompt was.

The Gap No One Talks About

Most people stumble into AI like tourists in a foreign city. They know roughly what they want but can't quite ask for it in the local language.

The difference between a mediocre response and something genuinely useful? Often it's just *how you ask*. Context matters. Format matters. The little instructions that feel unnecessary? They matter most of all.

Picture someone typing: *Write a caption for my Instagram post.* No mention of brand voice. No hint about tone. No clue about who's supposed to read it. The AI fills in the blanks with guesses — and guesses are rarely magic.

The people who get remarkable results from AI? They've learned to speak its language. They provide context, specify formats, include examples. They've invested time in understanding how to ask.

But most people don't have that time. They have emails to send, posts to write, ideas stuck in their heads that won't translate to the page.

What if we could close that gap?

A Simple Idea, Heavy With Possibility

The concept behind Prompt Pulse was almost embarrassingly simple: *What if we did the prompt engineering for you?*

Instead of users fumbling with the right words, we'd create pre-built prompts for common tasks. Each one would come loaded with:

• Context the AI needs but users don't think to provide
• Structure that guides the output into something usable
• Smart forms that ask for the right details, nothing more

So when someone needs to write an email, they don't stare at a blank screen wondering where to start. They pick the Email Writer, fill in a few fields — who's this for, what's the message, what tone do you need — and let Prompt Pulse handle the translation.

Behind the curtain, their scattered inputs merge with our carefully written template. The AI receives something rich and specific. The output? Dramatically better than anything a casual prompt could conjure.

The Build

I was the main developer on this one. A couple of months of focused work, then later a UX/UI designer came aboard to help smooth the rough edges.

The tech stack stayed lean:

• **Next.js** for everything — frontend, API routes, the whole conversation between user and AI happening in one project
• **OpenAI API** for the intelligence underneath
• **Firebase** for auth and storage

There's something satisfying about Next.js for projects like this. Backend and frontend live together. Changes ripple through naturally. You can move fast without fighting your own architecture.

The Experience We Wanted

Simple. Conversational. Invisible complexity.

When users land, they see a collection of prompt types. Email Writer. Caption Generator. The tools people actually reach for.

They pick one. A form appears — not overwhelming, just the essentials. They fill in what they know.

One click. The AI does its work. Output appears, ready to copy and use.

The magic was in what users *didn't* see. No prompt engineering. No token optimization worries. No wondering if they asked the right way. Just fill in the blanks, get quality out.

The Hidden Weight: Every Character Costs Money

Here's something most people never consider when they think about AI products: every word has a price.

OpenAI charges by the token — roughly chunks of words, in and out. The more detailed your prompt, the more tokens consumed. The longer the response, the higher the cost.

And here's the tension: the best prompts are often the longest. Rich context, detailed instructions, format specifications — all of it makes output better. All of it makes each request more expensive.

When users get comfortable with a tool, they use it constantly. That's success by one measure. By another? It can hollow out your margins faster than you'd believe.

We spent real time wrestling with token economics. Crafting prompts that were good enough to produce quality, lean enough to stay sustainable. It's the kind of trade-off that lives beneath every AI product, invisible to users, constant for builders.

What Actually Happened

I won't pretend Prompt Pulse became the next big thing.

It evolved into an internal tool for a digital agency. The head of the agency became our most dedicated tester, running it through their content workflows, finding the edges.

The feedback warmed me. The core concept worked. People who used it got better results than they'd have managed alone.

But we found rough edges too. Streaming responses that sometimes cut off mid-thought — the kind of UX bugs that separate a working prototype from something you'd proudly share with the world.

Right now, Prompt Pulse sits on a shelf. Waiting. Maybe a pivot is coming. Maybe a new skin for a different use case. The code isn't going anywhere.

The Threads I'll Keep

**Good ideas grow in conversation.**

Prompt Pulse started because someone mentioned struggling with AI prompts. One comment, one reply, and suddenly there was an idea worth building.

I've learned not to hoard ideas. Talk about problems. Float solutions. The best projects rarely emerge from solo brainstorming. They come from collisions — your thought bumping into someone else's need.

**AI skills compound.**

When I started this project, my AI knowledge was basic. I was figuring out API calls, stumbling through prompt engineering, learning token economics by getting surprised by bills.

That foundation has grown roots since. The chatbots I build now, the RAG systems, the integrations — they all trace back to these early experiments. Every project teaches something that makes the next one easier.

**Shipped beats shelved.**

Prompt Pulse taught me a familiar lesson in a new key. Internal testing catches some things. Real users catch everything. Those streaming bugs that frustrated our tester? They'd have surfaced faster with broader use. The prompts that worked best? We'd have learned quicker if more people had tried them.

Next time, I'll know. Get it out. Let it breathe. Fix what breaks.

Why This Matters to Me

Prompt Pulse won't top any list of impressive projects by scale or impact. But it represents a turning point: my first serious dive into building AI products.

It made me think about:

• How to make complex tech feel simple
• How to balance quality with cost
• How to design experiences that hide everything except what users need
• How to work with AI APIs when real money is flowing with every request

These questions have become central to my work. From WhatsApp bots to RAG systems, everything I build now stands on foundations Prompt Pulse helped me pour.

Sometimes the projects that matter most aren't the ones that launch biggest. They're the ones that teach you how to build what comes next.

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Technical Summary

**Timeline**: A couple of months of building

**Team**: Solo developer initially, later joined by UX/UI designer

**Status**: Currently shelved (potential future pivot)

**Key Learning**: The best ideas grow in conversation — don't keep them to yourself

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