Explainer · Prompt scoring
Score the prompt before you click generate.
Most wasted AI video credits come from generating first and judging after. Prompt scoring flips that: you check a prompt against the model’s known failure profile, fix the instructions it tends to drop, then generate once. This is the top-of-funnel explainer for how AVA thinks about consistency.
In one line
Prompt scoring is checking a prompt against a model’s documented failure profile before you generate — identifying which instructions that model tends to drop on your shot type, so you can fix the risky lines instead of re-rolling and hoping.
The core problem
Re-rolling is gambling, not fixing.
AI video models sample from random noise, so every generation is a fresh roll. When “run it four times and keep the best” changes everything between takes, the model is sampling rather than reading your prompt closely. The useful question is not which take is best — it is which instruction got dropped.
That dropped instruction is usually one rewritable line. Camera directions, exact object counts, and readable on-screen text are the first things almost every model sheds as a prompt gets longer. Fix that line and the “why is it ignoring me” mystery usually disappears — without burning three more credits.
Five steps
How to score a prompt before generating
- 01
Identify your dominant shot type
Face close-up, multi-cut character scene, crowd, text-on-screen, fast motion, or cinematic lighting. The model that stays consistent changes with the shot type — there is no single best model.
- 02
Look up that model’s documented failure profile
Each model drops specific instructions first. Camera directions, object counts, and on-screen text are the most commonly dropped across models. Check the failure catalogue for your model and shot type.
- 03
Score the prompt — flag at-risk instructions
Mark any instruction that matches a known failure mode for that model (e.g. “readable sign text” on a model that garbles text past six characters). Those are the lines likely to be ignored.
- 04
Rewrite or front-load the must-haves
Move critical instructions to the front, cut non-essential directives, and replace at-risk asks (small text, exact counts, complex camera moves) with post-production steps where possible.
- 05
Generate once, then log what happened
Instead of re-rolling blind, record which instruction the model actually dropped. That per-model history is what turns the next prompt into a one-take instead of a four-credit gamble.
Go deeper
Match a model to your shot
Consistency ranking
Which model is most consistent
9 models ranked by documented failure profile, shot type by shot type.
Failure reference
105 documented failure modes
The exact instructions each model drops, catalogued per model and shot type.
Head-to-head
Compare two models by failure profile
Side-by-side on the shot types where each one wins and loses.
Effective-cost calc
What a re-roll really costs
List price scaled by first-try success rate. No signup.
Answer engine
Common questions
What is AI video prompt scoring?
Prompt scoring is checking a prompt against a model’s documented failure profile before you generate — identifying which instructions that model tends to drop on your shot type. Instead of generating and judging the output, you score the prompt first so you can fix the risky instructions before spending a credit.
How do I make AI video prompts more consistent?
Front-load must-have instructions, keep prompts short, and avoid stacking many directives in one prompt. Models drop instructions as prompt length grows — usually camera directions, object counts, and on-screen text first. Putting the critical instruction early and trimming the rest is the single biggest consistency lever.
Why does the same AI video prompt give different results each time?
AI video models sample from random noise, so every generation is a fresh roll. If a re-roll changes everything, the model is sampling rather than reading your prompt. The fix is not more re-rolls — it is finding which instruction got dropped and rewriting that one line.
How do I stop wasting AI video credits?
Score the prompt before generating instead of re-rolling after. Blind re-rolling repeats the same dropped-instruction pattern, so it burns credits without fixing the root cause. Identify the at-risk instruction for your chosen model, rewrite it, then generate once.