How Agentic Editing Tools Decide What to Change When You Say "Make It Better"

When a video editing system receives instructions like “make it better,” then they get confused not about why, but because it doesn’t even specify which part needs work and improvements. When some comments are like “this feels slow,” at least they are making it clear about the work that needs correction. Whereas “make it better” means nothing specific, which leads to genuine questions: what actually happens when a system gets an instruction this vague? This is where agentic editing tools help.

Read the full blog to learn more about how agentic editing tools decide what to change when instructions are unclear.

Why This Instruction is Uniquely Hard

Most unclear instructions still point toward a category; “fix the color” is imprecise about how, but it’s specific about what. “Make it better” fails even that lower category; it could mean the pacing, the color, the audio, which means any system responding to this instruction is working with actually less information than even a modestly vague request provides.

What A System Can Reasonably Do With It?

Faced with this, a system can fall back on normal editorial heuristics, checking for the kinds of issues that commonly hold a video back: a section with unusually low pacing compared to the rest of the project, a color treatment that shifts noticeably from a technical baseline, an audio level that’s inconsistent with the surrounding sections. This isn’t checking the requester’s mind; it’s applying the same general judgment an experienced editor might use when scanning a cut for anything that stands out as off, in the absence of being instructed what specifically to look for.

Why This is Fundamentally A Guess, Not A Diagnosis

It’s worth being honest about what this really is: an educated guess based on generic signals, not a targeted fix for an issue the system actually understands. Two different videos both getting the instruction “make it better” might have completely different real issues, and a system applying general heuristics has no way to understand which specific thing the requester was reacting to when they gave that instruction. Treating the result as a confident, correct fix instead of a first attempt worth reviewing is the real mistake here, not the system’s guess itself.

What A Genuinely Good System Does Differently

The better version of this behavior isn’t pretending to solve the ambiguity with confidence; it’s making a modest, clearly reviewable change and being honest about what it changed and why: “tightened the pacing in the middle part, since it read noticeably slower than the rest of the project.” That transparency turns a guess into something a person can immediately evaluate and correct: “actually it was the color, not the pacing,” instead of a silent change the requester has to reverse-engineer if it doesn’t match what they meant.

The Better Alternative: Nudging Toward Specificity

The most reliable path here isn’t a good guess; it’s a lighter prompt toward the actual problem: surfacing a normal change and inviting a correction, or in some cases directly asking which dimension feels off before attempting anything. An invideo online video editor that treats “make it better” as an opening move in a conversation, instead of a final instruction to resolve silently, gets to the actual intended fix faster than one that tries to assume perfectly on the first attempt and hopes it landed.

Conclusion

“Make it better” doesn’t have a good single-shot resolution, because it doesn’t specify what dimension requires work, only that something does. A reasonable system responds by applying general editorial heuristics to make a modest, transparent first try, treating the result honestly as a guess instead of a confident fix, and making it easy for the requester to redirect if that guess missed the real problem. The instruction that actually gets a video better isn’t “make it better”; it’s the more specific follow-up that conversation often produces.

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