How I Fixed Dozens of Bugs & Improved UX by Dogfooding My Own AI Product (Studdymate AI)
Sharing my experience with fixing bugs and improving user experience in Studdymate AI—and how using my own product daily helped me catch critical issues faster and ship better updates quickly.
Btw, we’re fully live now, and most of our early improvements came directly from real usage within the first few days. Every bug fixed immediately improved retention—which is something you only truly understand when you dogfood your own product.
Here’s what I did:
I started using Studdymate AI like an actual student, not a builder. I intentionally pushed edge cases—complex questions, long sessions, repeated queries, and different devices (mobile + desktop).
At the same time, I closely monitored user behavior:
Where users dropped off
Where responses felt slow or confusing
Which features were ignored or misunderstood
Whenever a bug or friction point appeared, I didn’t just fix it—I traced the root cause and simplified the flow around it.
I also encouraged early users to share raw feedback through DMs and directly observed how they interacted with the product.
The numbers:
• Dozens of micro-bugs identified and fixed within days
• Noticeable drop in user drop-offs after key fixes
• Improved session time and repeat usage
• Faster response consistency across devices
The reason it worked:
I wasn’t relying on assumptions or waiting for large-scale data. I experienced the product exactly like my users did—at the moment of friction.
Most early-stage products fail not because of lack of features, but because of small UX issues that stack up. Fixing those quickly created a smoother experience and built trust with early users.
Still iterating, still refining—but this process helped me turn early chaos into a more stable and usable product as a solo founder.
Happy to answer questions if anyone wants to build and improve this way
