From Problem to Product: My AI-Assisted Shortcut to Smart Business Solutions

I recently tackled a core feature for my app that, traditionally, would have meant weeks of meetings, diagrams, and debates. I’m talking about building a smart recommendation engine – one that doesn’t just suggest things nearby, but also considers the time of day, the weather, and even who you’re with. Imagine recommendations that truly understand your current vibe!

Normally, a task like this involves a lengthy process: multiple stakeholder meetings to hash out ideas, product requirement documents that grow into small novels, intricate architecture diagrams, and a good old-fashioned back-and-forth until everyone’s on the same page.

But this time, I decided to try something different. Going solo. Okay, me and two partners: ChatGPT and Google Gemini. They weren’t just tools; they were my:

• Product Strategist
• Urban Data Analyst
• Backend Architect
• UX Brainstorm Partner

And the result? In a single day, we went from a handful of vague goals to a crystal-clear feature specification. It felt like magic, but it was really just a smart co-pilot loop.

The AI Co-Pilot Loop: My New Design Workflow

Here’s how this real-time partnership unfolded:

1. Starting with the Vision

I kicked things off with a simple prompt: “I want to give users recommendations based on context like weather, time, and who they’re with — how should we build it?”

The AI instantly responded with incredibly useful insights. It suggested key signals to consider (like crowd size and seasonality), proposed a clean backend architecture with smart caching, and even offered different “trigger models” for when recommendations should refresh (daily, on change, on demand). Suddenly, this nebulous feature had a solid structure.

2. Zooming into Edge Cases

Next, I pushed on potential pitfalls, like what happens in less populated areas: “What if someone’s in a small town? Or a huge city with dozens of distinct neighborhoods?”

This is where the AI truly shone as a brainstorming and research partner. I specifically leaned on Google Gemini’s research agent here. Rather than spend hours myself digging through demographic data and urban planning classifications, I tasked it with finding existing geographical grouping strategies for Canada and the US. It came back with a comprehensive list of different approaches – everything from official statistical area definitions to more dynamic ways of clustering urban zones. This allowed us to brainstorm incredibly quickly on:

• Dynamic grouping of urban zones (e.g., merging smaller areas into a “Tri-Cities” cluster for better recommendation density).
• Context-aware fallback logic for low-data areas, ensuring users in remote locations still get relevant suggestions.
• When and how to merge city clusters for better recommendation density, all backed by research on how these areas are typically classified.

These are insights that typically surface much later in the development cycle, often after significant effort has already been invested. Catching them early saved a ton of potential rework.

3. Cost and speed (without killing user experience)

I knew that frequent, real-time API calls for every user’s recommendation would quickly burn through my budget. So, I challenged the AI: “How can we optimize this for scale and cost?”

The solution it helped me craft was brilliant:

• Batch-generate ideas per city and time block.
• Cache them efficiently in my database (Supabase, in this case).
• Let the client filter and rank by real-time context.

The outcome was a win-win-win: faster response times, lower API usage, and a much cleaner architecture.

4. UX ideas that came before the UI

Even before I opened Figma, the AI was helping me think like a UX designer. It offered ideas for:

• A simple voting mechanism (👍👎) to continuously improve recommendations.
• The option for users to “promote” their own suggestions, fostering community.
• Smart client refresh strategies based on app usage patterns.

It genuinely felt like having a dedicated UX teammate right there in the room with me, offering fresh perspectives.

Why This Partnership Was a Game-Changer

This wasn’t about AI doing the work for me; it was about AI working with me as a truly collaborative partner. The benefits I saw were immense:

• Explored product edge cases at lightning speed, powered by quick research.
• Iterated on architecture in the earliest stages.
• Generated markdown-ready specs on the fly, saving documentation time.
• Made informed tradeoffs with incredibly low friction.

What normally takes a week or more of dedicated effort was condensed into just a few hours.

What You Can “Steal” From This Workflow

Don’t wait until you’re deep into development to bring AI into your loop. Pull it into your early design and planning phases!

Try using AI for:

• Product ideation: “For a fitness app, how could it keep users motivated after they hit their initial goals?”
• Infrastructure planning: “What’s a smart way to cache frequently queried local events to reduce database load?”
• UX mechanics: “How can I design a progress bar that genuinely encourages task completion?”
• Feedback loops: “What lightweight ways can users signal they love (or hate) a suggestion?”

You’ll be genuinely surprised at how much clarity and progress you can achieve before you even draw your first wireframe.

Final Thought

This experience wasn’t just about ChatGPT or Gemini spitting out code. It was about AI acting as a profound thought partner – an integral part of my creative process. It helped accelerate clarity, surface blind spots I might have missed, and ultimately shape a system that’s both scalable and truly user-first.

And in 2025, that kind of AI-powered clarity is a superpower every engineer should be leveraging.

👉 Over to You

Have you used AI in unexpected ways beyond just code generation? What’s the most surprising way it helped shape your product or project?

#NoviceVibeCoder #BuildWithAI #ProductDesign #TechLeadership #AIInSoftwareEngineering #MVP