Generative AI is not a magic button that gives you the perfect result every time.

It’s more like a lottery.

One prompt hits the target, another one fails, the next is “almost there,” and sometimes the model just breaks.

If we look at user experience, there are four common outcomes:

1. Perfect hit — exactly what the user wanted.
2. Partly successful result — some useful parts, but needs more work.
3. Off-topic — not what was asked for.
4. Empty or broken — generation failed completely.

For UX, these are not four different products — they are four states of the same process.
A user can go through all of them in one evening.
That’s why the interface should not promise “only perfect results.”
It should help people recover safely from any outcome.

Today we talk about the first one — when the model gets it right.
It seems like the work is done.
But actually, it’s just starting.

Even when the AI gives a great image, text, song, or slide — users rarely stop.
They want to compare, check, and save versions.
They want to be sure it wasn’t just luck.

That’s why the interface must support working not with one result, but with many versions.

To avoid chaos, an AI interface should offer a few simple tools:

• Likes or tags. Mark results as “good” or “bad.”

⚠️ Today, these marks are usually only for user navigation - the model doesn’t always learn from them.

• Notes. Let users write short comments for each version - like “good rhythm,” “nice light,” or “better tone.”

This small thing really helps later: users can remember what was good without rewatching or rereading everything.

• Сatalog and filters. Search by time or features — for example: “show versions from yesterday” or “dark backgrounds only.”

With these tools, even if the user already has a good result, they can keep exploring without fear of losing progress.

It’s natural to think:

“What if I take the character from this image, and the composition from that one - that would be perfect!”

The problem is, today most AI models can’t really combine finished pieces by command.

Right now, users can only:

1. Use versions as references. Show the system what to look at, but the new result will still be unpredictable.
2. Combine manually. Use design or editing tools — Figma, Photoshop, a DAW, etc.

Still, UX design can help here.

Even if the model can’t merge results, users can organize their ideas — mark favorite parts, keep notes — and that data can become the base for future AI features.

Even a perfect AI result doesn’t mean trust.
Trust grows when the user can:

• compare different results;
• see that success is repeatable;
• return to the right version later.

An interface that gives control over versions reduces anxiety and helps users build their own workflow on top of an unpredictable system.

This was the easiest case — when AI gets it right.
Next comes the harder one:
What if the result is almost good, but needs changes?
How can users tell the model what exactly to fix?
And how not to drown in dozens of similar versions?

That’s what we’ll explore in Part 2.