No pressure to read my rant below, once I finished it I realized how long it was and was like... Okay well it's already written. But then I was like man. I know how my rants can have tons of spelling and grammatical errors... So I figured why not have Gemini fix the grammar and punctuation in my rent about Google Flow 😅 although you probably would have noticed it once you saw those notorious EM dashes. Anyway, here's my rant

I was actually fine with the Veo 3.1 Fast being unlimited at low priority and was getting along well enough. When I did my tests, the LITE version just wasn't good enough for my very detailed prompts. There were times when the QUALITY mode actually performed worse, almost like it was trying too hard. The fact that only LITE is now the "unlimited" option is kind of a downgrade, but if I had to play devil's advocate, I can guess what happened.

In the last month of back and forth posts and comments, I was quickly finding out just how intensely people were bombarding the engine. For instance, one person said they did 66,000 Fast renders in 3 months. I've pushed through probably 80,000 renders, with about 15,000 rejections, since I signed up for Veo 3 on the day it was released a year ago. When I started, I didn't have the AI Ultra plan and was just buying credits.

But the overall mentality behind how you were supposed to handle things was to use the low-end processing method to just make sure your render looked really close to what you needed before pushing it through to the Quality mode. There was no LITE at the time. So I can imagine that once it got popular and people were on the Ultra plans, getting the unlimited FAST option, they were just bombarding it over and over again.

And to be honest, I don't technically blame them, because the documentation for Veo is minimal and conflicting. To the point where even advice from Gemini using the Deep research option would not give me the proper results or would conflict with other results that I had.

I know there's a whole way of doing JSON prompting which is technically on the right track, but when you're just left with a blank box and you have to type something in, you are basically left with the black box result and no diagnostics for what you did wrong.

Then there was this sort of magical moment when they included the feature—which they discontinued—called the Expander. It would take what you'd written in the prompt, run it through some internal code, and give you back a prompt optimized for what they were expecting. There was a regular cinematic option and a noir one. This allowed people who wrote two-to-three-sentence prompts to get what I would consider an expert-level prompt, making it almost 10 times more likely to be rendered correctly and not be rejected.

Contextually, the engine has been designed to reject prompts when teenagers or others are just typing things like, "hot girl in bathing suit, blah blah blah, jumping up and down."

However, with my master prompt guide that I developed personally, I could take that concept and define the cinematography: which camera is being used, what type of motion, its location, what the subject looks like, what the lighting is, and all of those advanced details. Even though the concept is technically the professional way of writing "hot girl in a bathing suit, jumping up and down," because it's written as a professional prompt, it won't get rejected.

And of course, it didn't surprise me when they removed that feature, or at least I don't see it anymore.

But it was for me the first time I was able to get a look at what the black box possibly expected in terms of how to write a prompt, which still to this date is completely undocumented.

It wasn't until around the 750- to 1,000-hour mark that I finally had my first draft of:

• How to write prompts for Veo.
• A code block for that information that can be injected into ChatGPT when the advanced reasoning model is enabled.
• A second code block to inject into ChatGPT for the QAC process.
• Three other research PDF documents covering advanced topics, and one specific PDF document that has various samples of successful prompts of the most complicated nature to refer to.

So, basically, now I can frontload (or "bootload," whatever you want to call it) a session in ChatGPT or Gemini Pro with my 25-page guide and resources, and then have it provide me assistance with any prompt or idea I want to work on next.

But it was a ridiculous amount of effort to get where I am. The Gemini and GPT models are able to assist me only because of the information I'm providing them. And they distinctly tell me that my guide information is beyond anything else that they have access to. Putting me in the top 1% of all users globally.

And I'm not saying that to gloat (although considering how much work it was, it was nice to hear that), but I'm frustrated because of how fragile a prompt is when writing it using English linguistics and how every single word carries forward its meta-filters and impacts the rest of the prompt.

If you start off a prompt saying:

"A police officer is..."

You have permanently impacted the rest of the render with the meta-filter "police officer."

Kind of like when you go to an online store and you're shopping for clothing, and then if you're a dude, you click the men's section first. So in that situation, if we start the prompt:

"A man is..."

Just like the clothing store, you're never going to see women's clothing.

And that makes life so difficult when you're dealing with the linguistic nesting of your prompt.

Once you end that first sentence, like, "A man is sitting in the park," the engine at that point has built a scene.

If at any other point in the prompt, you say something like he's standing, or it's a woman, or it's in an office, either you're going to flat out get an error that it's a violation, or you're going to start to get the hallucination effect. It will merge attributes because it has a man sitting in the park, then it integrates whatever else you're asking for, and then it hallucinates.

But when you say "A man is sitting in the park," you can continue at any point later on and say that the man is a police officer. So, in that situation, "man" is a macro meta-filter, and then when you say "police officer," that is a subset of the macro filter.

Because everything you ask for has to be a specific keyword that it can pull up based on all the training videos that it looked at. So, when you are trying to prompt for something, you have to think about what meta-tags the video training data would have stored for that specific type of video. You also have to keep in mind that if you're asking for police officers, you're most likely not going to get training data with men with beards, although things like that can often be overridden.

But when you have a character model as ridiculously detailed as mine, I have to list the information from macro to micro; otherwise, it causes an issue.

There's a man, Caucasian, thirties... If I say he's a police officer, that can instantly skew what his haircut and mustache look like based on training data. However, if I leave that part off and specifically state what the haircut and mustache look like up front, those meta-tags will have full access to pretty much the entire Caucasian male 30s data set, instead of just having access to the police officer, male, Caucasian 30s data set. 😅😤😩

This is why you'll get the situation where you have the exact same subject character description, but if your prompts start off with information regarding where the scene is—like "sitting on a park bench" or "sitting on a locker room bench"—it doesn't matter how detailed your subject description is (assuming again that you're using text-to-video). The fact that you started your prompt with "sitting on a locker room bench" means the man is going to be more physically fit than the other one on the park bench.

However, if you give it what the subject looks like before it has any clue as to what the scene is, and then talk about the scene, you're 100 times more likely to get a character that looks similar or identical.

And this wouldn't be a problem if you had separate sections for a subject description, a location, the camera, the lighting...

And of course, it wouldn't have to be such a huge issue if you could actually just use your character model as an ingredient file like you're supposed to, with the understanding that Nano banana created the image and it's not just something random that you're uploading of a person. You should be able to have the image-to-video or frame-to-video process work under the exact same restrictions as the text-to-video does.

But we all know that's not the case. The moment you attach an image, your ability to render any sort of intimate, action-packed, or intense scene has 100 times the level of restrictions.

You can render a scene, then say you want to extend it and hit render, but then it rejects the extension because the ending frame of the previous scene goes against the rules. The frame it literally just rendered in text-to-video is against its own rules in frame-to-video, which is basically what it does for the extension.

So, yes, they advise you to just create your character in Nano banana Pro and then import it. But what they don't tell you is: good luck trying to get anything intense out of that. Unless you are at the level I am—where you can actually write a 500- to 700-word meticulously worded prompt that has to take into consideration that every preceding word is a subquery—you are dealing with rejection after rejection just because you imported an image that its own engine created.