Picture this: you fix one flaw in your AI video prompt, regenerate, and the result somehow looks worse than the version before it. That’s not bad luck. According to u/Fun_Walk_4965, a Redditor who broke this down in r/PromptEngineering, it’s a specific failure mode with a name: “keyword pollution.”
Here’s the quick version before we get into it.
Quick Start
- Stop describing what you don’t want. Describe exactly what’s visible right now.
- Swap vague “power words” (professional, cinematic, tense) for concrete visual details.
- Rebuild each revision from scratch instead of patching the last one.
The Old Way vs. The New Way
Most people iterate on AI video prompts by piling on corrections. Something looks off, so they add a line telling the model what to avoid. The prompt grows, the corrections stack up, and the output somehow drifts further from what they wanted.
The old way looks like this:
Don’t use the previous outfit.
Don’t show that prop again.
Don’t use the old pose.
Don’t make it look like the last version.
The problem, as the original poster points out, is that the unwanted concept is still sitting inside the prompt. The model reads “red jacket” even inside a sentence that’s trying to ban it. The mistake never actually leaves the text.
The new way flips that. Instead of naming the flaw, you describe the fix directly:
The character wears a dark gray short jacket with clean tailoring.
That’s the whole shift: stop narrating the previous mistake, and describe what should be visible now. Short version, sharper result.
Why “Power Words” Backfire
This Reddit user’s second finding is just as useful. Words like “professional,” “premium,” “cinematic,” “futuristic,” “tense,” and “luxury” aren’t wrong exactly, they’re just heavy. Each one drags along a whole bundle of visual associations the model fills in on its own, and you lose control over which ones show up.
The fix isn’t banning these words. It’s asking one question: does this word describe something the camera can actually see? If not, translate it into visible detail.
- “Professional” becomes posture, clothing shape, hand position, background complexity, lighting, subject placement.
- “Tense atmosphere” becomes shoulders slightly tightened, body leaning forward, hands paused before movement, harder side lighting, compressed background space.
Visible evidence beats abstract intent, every time.
Practical Steps: Rebuilding a Prompt From Scratch
The full workflow the author shared breaks into five moves. Here’s each one, with the reasoning behind it.
- Stop repeating the previous mistake. Every “don’t” statement keeps the unwanted concept alive in the prompt. Replace it with a direct description of the current state instead.
- Watch for high-association keywords. Words like “cinematic” or “battle” pull in a whole visual package. Ask whether the word describes something the camera can literally see.
- Turn abstract ideas into visual evidence. List out posture, clothing, hands, background, and lighting instead of a single adjective. Concrete beats vague.
- Don’t leave camera terms as labels only. “Close-up” or “overhead shot” isn’t enough on its own. Add what the camera actually sees, so the structure becomes camera type plus visible information plus spatial relationship. Example from the post: “Overhead shot. The camera sees the top of the table, spacing between objects, and both hands entering from the bottom of frame.”
- Treat every revision as a fresh shot. Instead of patching the last attempt, rewrite it start to finish: subject, environment, body direction, hand position, object state, camera, lighting. That order alone tends to clean up a messy prompt fast.
The Cleanup Checklist 🎬
Before generating, the author runs through seven questions:
- Does any word trigger unnecessary visual associations?
- Am I repeating a mistake from the previous generation?
- Am I using words like “previous,” “again,” or “not like before”?
- Can abstract words be replaced with visible details?
- Does the camera description explain what is actually visible?
- Are negative prompts limited to general failure types?
- Am I changing only one major variable this round?
That last question matters more than it sounds. Change one variable per round and you can actually tell what fixed the shot. Change five and you’re guessing again.
The community response backs this up. One commenter called it “a rare thing to find good prompt advice out there.” Another said they’d been doing something similar without ever naming it “keyword pollution,” which is exactly why frameworks like this one are worth stealing: they turn a gut feeling into a repeatable checklist.
The main takeaway, straight from the source: AI video prompts don’t need to get longer to get better. They need to get more visible, more specific, and easier to debug. Or shorter still: stop describing the previous mistake, describe the current shot.
Try rewriting your next revision from scratch using the five-step order above, then run it through the checklist before you hit generate. If your outputs have been getting worse with every fix, this is probably why. Head over to the original discussion in r/PromptEngineering to see the full thread and the replies from other prompt engineers testing the same approach.
Frequently Asked Questions
Q: Should I stop using words like “cinematic”?
No, just be intentional about it. As one reader noted, “cinematic” brings a whole visual package, lighting, color grading, mood, you might not have explicitly asked for. That’s not bad, just worth knowing. If it fits your vision, use it. If you’re getting unintended results, now you know why. Swap it for the specific visual elements you actually want instead.
Q: How do I spot keyword pollution in my own prompts?
Look for abstract words that don’t describe something the camera can see: “professional,” “premium,” “tense,” “luxury.” Also watch for negative instructions like “Don’t use the red jacket.” Replace them with concrete details, posture, clothing, lighting, hand position, composition. If it’s not visually specific, it’s probably carrying baggage.
Q: How do I test if my revised prompt is actually cleaner?
Remove a vague word and describe those visual details explicitly instead. Generate a few outputs and compare side by side with your previous version. You’ll usually notice the results are more consistent and have fewer surprise elements. That’s your signal that keyword pollution is gone.
Q: What if mood or vibe is crucial to my video?
Translate it into visible evidence. Instead of “tense,” describe the physical signs: shoulders tightened, body leaning forward, hands paused, harder side lighting, compressed framing. This gives the AI concrete visual targets instead of abstract labels it has to guess at.
AI Video Prompting Guide: How to Avoid “Keyword Pollution” and Fix Prompts More Reliably
by u/Fun_Walk_4965 in PromptEngineering