The Hardest Part of Building an AI Editor Isn't the AI

The hardest part of building an AI editor isn't the AI. It's defining what "good" means.

When you build a code generation tool, you can unit test the output. When you build a search engine, you can measure relevance with click-through rates. Clear metrics. Clear feedback loops.

When you build an AI that edits video, "good" is subjective. Two editors can watch the same rough cut and disagree on whether it works.

So how do you train a system to make editorial decisions?

You don't start with the AI. You start with the editors.

We spent weeks watching professional editors work. Not interviewing them about their process. Actually watching them make decisions in real time. Where they scrub. What makes them stop. When they mark an in-point versus when they skip ahead.

Patterns emerged that editors themselves couldn't articulate.

They almost never select the first answer a subject gives. They wait for the second or third take, when the person relaxes and speaks more naturally. They listen for drops in vocal pitch, which often signal conviction. They cut away from a clip the moment energy drops, even mid-sentence.

These aren't rules you'll find in an editing textbook. They're intuitions that take years to develop.

We encoded those patterns into our narrative engine. Not as rigid rules, but as weighted signals that inform clip selection and sequencing.

The result: when editors review our AI-generated rough cuts, the most common reaction isn't "this is wrong." It's "how did it know to pick that moment?"

It knew because we watched editors pick that moment hundreds of times first.

Building AI for creative work requires a different kind of training data. Not labeled datasets. Observed intuition.

What's a decision in your field that seems intuitive but actually follows hidden patterns?