It is so easy to fall into the trap of thinking that adding more text fixes bad outputs. We used to write massive instructions packed with edge cases and the model would end up ignoring half of them anyway.
Trimming everything down to clear bullet points and simple variables made the responses way more reliable. Cutting out the fluff really does force the model to focus on the actual task.
Do you prefer keeping your system instructions short or do you like adding strict guidelines for formatting?
Looking at who pays in my app, the split by language surprised me. Spanish-speaking users convert at around 12%, Arabic-speaking users at about 2.5%, even though Arabic users found the app on their own and used it a lot.
I never localized the store page for either.
Is this mostly purchasing power, store pricing tiers by country, or trust in a small unknown app?
Been thinking about this a lot lately most teams either rely on OS-level deletion or full-drive wipe tools, but neither really covers targeted cleanup (specific files, cloud drive residue, browser traces) for GDPR/HIPAA-style audits. Curious how others handle this in practice is it a tooling gap, or more of a "nobody owns this process" problem in most orgs?
There is nothing quite as humbling as reading a cancellation reason that says: "Great product, but we just didn't use it enough to justify the cost."
It s not a bug, it s not a broken feature it s just indifference. But those brutal honesty suryeys have secretly taught us more about our product than all our positive user reviews combined.
How do you structure your offboarding flow to actually get honest feedback from churning users without annoying them on their way out?
When I started I thought I knew exactly what people would care about. I had the features planned out and a pretty clear idea of what the product should become. Then actual users started asking for things I never expected and some of the things I thought were important barely mattered to them. It was a little frustrating at first but it also made me question how much of the original plan was based on assumptions. How much do you let user feedback change the direction before it stops feeling like your original idea?
When we first launched, we celebrated every activation milestone like a major win. But a few months in, we noticed a brutal pattern: users hit our "Aha!" moment, loved the onboarding, and still churned by week three.
Turns out, experiencing value once isn't the same as building a habit.
For those running SaaS products, how do you bridge the gap between initial onboarding and making your tool an absolute daily non-negotiable? Do you rely on lifecycle emails, in-app nudges, or something else entirely?
One hard lesson from onboarding paid users: people don't churn because you lack features. They churn because your tool disrupts their workday routine instead of simplifying it.
Fixing habit-loop integration is a much tougher challenge than shipping another update.
How do you bridge the gap between initial signup and daily routine adoption?
My first major product launch was an absolute mental roller coaster. I was so paranoid about things breaking that I stayed up all night checking the listing page then spent the entire morning glued to live visitor stats.
It realized pretty quickly that obsessing over every single view is totally unsustainable.
Hi there, well :) not much to add here, my Q is what I'm sharing in the title. Maybe just to add a bit of context, I'd like to have smth I won't pay for here :)
