You know what’s funny? I used to think the point of using AI was to save time. Makes sense, right? Instead of spending hours writing something myself, I’d just ask ChatGPT and get instant results.
Except that’s not what happened. Not even close.
What actually happened was this weird dance where I’d spend forever trying to get AI to understand what I wanted, going back and forth like we were having some kind of passive-aggressive argument. Sometimes I’d spend more time “talking” to ChatGPT than it would’ve taken to just write the damn thing myself.
Those frustrating conversations eventually taught me everything I needed to know about building better prompts. But man, the learning curve was brutal.
The Great Email Disaster of January
Picture this: It’s January 15th, around 10:30 AM. I need to write a promotional email for my client’s new online course about photography basics. Simple enough, right?
Me: “Write a promotional email for a photography course.”
ChatGPT: Gives me this generic thing that could’ve been selling literally any course about anything. Zero personality, no specific benefits, just marketing fluff.
Me: “Make it more specific to photography.”
ChatGPT: Adds some camera terminology but still sounds like it was written by someone who’d never held a camera.
Me: “This course is for beginners who are intimidated by their camera settings.”
ChatGPT: Better, but now it’s talking down to people like they’re children.
Me: “Don’t be condescending. These are smart adults who just haven’t learned camera basics yet.”
ChatGPT: Swings too far the other direction, now it’s assuming people know more than they do.
Me: “Ugh, forget it. Start over.”
By attempt seven, I was ready to write the email myself. Which I should’ve done from the beginning because I wasted forty-five minutes on what should’ve been a ten-minute task.
But here’s the weird part – that frustrating conversation taught me exactly what information AI needed upfront. Audience sophistication level, specific fears and motivations, brand voice, course positioning. Everything I eventually fed it in tiny pieces should’ve been in my first prompt.
The Social Media Spiral of Doom
February was worse. I’m helping this local bakery with their Instagram content, and I fall into the same trap but deeper.
Me: “Create Instagram posts for a bakery.”
AI: Generic food photos descriptions that could work for any restaurant anywhere.
Me: “This is a specialty bakery that makes custom cakes.”
AI: Focuses only on custom cakes, ignores their daily pastries and coffee.
Me: “They also sell regular pastries and coffee every day.”
AI: Now it’s listing everything they sell like a menu instead of creating engaging social content.
Me: “Make it more engaging. People should want to visit.”
AI: Adds exclamation points and emoji suggestions. Still boring.
Me: “Their customers are mostly young professionals who stop by before work.”
AI: Finally getting somewhere, but now it’s all about rushing and convenience.
Me: “But they also want to highlight the artisanal quality and local ingredients.”
AI: Tries to balance rush and artisanal, ends up saying nothing meaningful.
Fifteen prompts. FIFTEEN. For something that should’ve taken one well-crafted prompt.
I’m sitting there thinking, “There has to be a better way to do this.”
The Blog Post Marathon That Broke Me
March 8th. I’ll remember that date forever because it’s when I finally snapped.
I’m working on a blog post about sustainable home gardening for a client in the eco-friendly space. Seems straightforward, but somehow I turn it into a three-hour nightmare.
The conversation went something like this:
“Write a blog post about sustainable gardening.” ↓ “Too broad, focus on home gardens for beginners.” ↓
“Not beginners exactly, people who’ve tried gardening before but want to be more sustainable.” ↓ “Include specific techniques, not just general advice.” ↓ “Make it practical for people with small yards.” ↓ “Also include apartment dwellers with balconies.” ↓ “The tone should be encouraging but not preachy.” ↓ “Add some science but keep it accessible.” ↓ “Include cost-saving benefits, not just environmental ones.” ↓ “Mention specific plants that work well for sustainable methods.” ↓ “Actually, organize it by season so people know when to do what.”
By prompt twelve, I had something decent. But I’d reconstructed the entire brief through trial and error instead of thinking it through upfront.
That’s when I realized I was treating AI like a mind reader instead of giving it proper instructions.
The Product Description Purgatory
April brought its own special kind of torture. Client sells handmade soaps at farmers markets and wants product descriptions for her new website.
Simple request, right? “Write product descriptions for handmade soaps.”
What followed was an hour-long back-and-forth that felt like pulling teeth:
First attempt: Generic soap descriptions mentioning “luxurious lather” and “moisturizing properties.”
“These are natural soaps made with essential oils.”
Second attempt: Lists ingredients but reads like a chemistry textbook.
“Write for people who care about natural products but aren’t chemists.”
Third attempt: Better, but ignores the handmade, small-business angle.
“Emphasize that these are handcrafted by a local artisan.”
Fourth attempt: Now it’s all about the maker but nothing about the soap benefits.
“Balance the personal story with product benefits.”
Fifth attempt: Getting closer but the tone is wrong.
“Sound more like talking to a friend, less like an advertisement.”
I’m watching the clock, seeing my hourly rate plummet with each revision. There had to be a more efficient way.
The Lightbulb Moment That Changed Everything
The breakthrough came in May during what should’ve been a simple project. Writing website copy for a local accounting firm.
I’m twenty prompts deep, getting increasingly frustrated, when I decide to step back and analyze what’s happening. I grab a notebook and start writing down every piece of information I’d gradually fed to ChatGPT:
- Industry: Accounting
- Business size: Small local firm
- Target audience: Small business owners
- Audience pain points: Overwhelmed by taxes, don’t understand regulations
- Desired action: Schedule consultation
- Brand voice: Professional but approachable
- Competitive advantage: Personal attention vs big firms
- Local focus: Community connections matter
- Specific services: Bookkeeping, tax prep, business consulting
- Seasonal considerations: Tax season urgency
Looking at that list, I realized something obvious: If I’d included all this information in my first prompt, I would’ve gotten a decent result immediately instead of spending an hour training AI through repetitive conversations.
That notebook page became the blueprint for what would eventually become Jason.
The Pattern I Couldn’t Unsee
Once I started paying attention, the pattern was everywhere. Every frustrating AI conversation followed the same script:
- Vague initial prompt
- Generic, unhelpful response
- Adding context piece by piece
- AI slowly getting better as information accumulates
- Eventually reaching something useful after way too many iterations
It was like giving someone directions by having them drive around randomly until they accidentally ended up in the right neighborhood.
The solution was obvious: Front-load all the context instead of feeding it in drips.
The Birth of My Prompt Framework
By June, I started developing what I called my “Context-First” approach. Instead of jumping straight into requests, I’d spend five minutes thinking through:
Role Definition: Who is the AI supposed to be? Audience Analysis: Who exactly are we writing for?
Goal Clarity: What specific outcome do we want? Context Setting: What background information matters? Voice Guidelines: How should this sound? Constraint Specification: What limitations apply?
Those time-wasting conversations had taught me exactly what questions to ask upfront.
My first test: Writing email copy for a client’s product launch. Instead of my usual approach, I spent five minutes building a comprehensive initial prompt.
One prompt. One response. Done.
The email was better than anything I’d gotten from twenty-iteration conversations, and it took six minutes total instead of an hour.
The Validation That Shocked Me
July was when I started sharing my approach with other freelancers and content creators. The feedback was eye-opening.
“Holy crap, this actually works.” “I’ve been doing AI completely wrong.”
“This saves me hours every week.” “Why doesn’t everyone know about this?”
But here’s what really surprised me – people kept asking if I had a tool that could automate the context-gathering process. They understood the concept but found it hard to remember all the elements consistently.
That’s when I realized there was a gap between understanding good prompting theory and actually implementing it reliably.
The Tool That Had to Exist
By August, I was sketching out what would become Jason. Not because I had grand entrepreneurial ambitions, but because I was tired of manually building context-rich prompts for every single project.
The concept was simple: What if there was a tool that walked you through the context-gathering process and then built the comprehensive prompt automatically?
Instead of hoping people would remember to include audience analysis, goal clarity, voice guidelines, and constraint specification, the tool would prompt them for this information systematically.
No more time-wasting conversations. No more forgetting crucial context. No more gradually training AI through twenty iterations when one good prompt could do the job.
What Those Conversations Really Taught Me
All those frustrating back-and-forth sessions with AI weren’t wasted time – they were expensive education. Each failed conversation taught me something about what information AI needs to produce useful output.
The email disasters taught me about audience sophistication levels. The social media spirals showed me why brand voice matters. The blog post marathons revealed the importance of structural guidance.
The product description purgatory highlighted the need for competitive positioning.
Every inefficient conversation became a lesson in what to include upfront instead of discovering through trial and error.
The Irony of AI Efficiency
Here’s the funny thing about using AI to save time: It only saves time if you know how to use it efficiently. Otherwise, it’s like having a Ferrari but not knowing how to drive stick shift.
Most people approach AI the way I used to – jump in with vague requests, then spend ages trying to course-correct through follow-up prompts. It feels productive because you’re “collaborating” with AI, but you’re actually just doing inefficient prompt engineering in real-time.
The efficient approach feels slower at first because you spend time thinking before prompting. But that upfront investment pays off immediately with better results and zero revision cycles.
Why This Still Matters
Even now, I see people making the same mistakes I used to make. They treat AI conversations like brainstorming sessions instead of instruction delivery.
There’s nothing wrong with brainstorming, but it’s not the most efficient way to use AI for content creation. When you need specific outputs for specific purposes, clarity and context upfront will always beat iterative discovery.
The conversations that taught me this lesson were painful at the time, but they led to understanding something crucial: AI isn’t magic, and it’s not a mind reader. It’s a very sophisticated tool that produces excellent results when given excellent instructions.
Jason exists because those time-wasting conversations taught me exactly what excellent instructions look like.
What’s the longest AI conversation you’ve ever had that should’ve been one prompt? Drop a comment below – I bet we can figure out what context you were missing.