AI prompt generator and builder

Ready-made prompts for photos and photoshoots, chat, study, work, video, code and music — plus a builder that turns one phrase of yours into a full prompt. A 0–100 score shows which meaning-carrying parts are missing: tap any of them right inside the prompt and fill it in. For ChatGPT, Midjourney, Nano Banana, Sora, Suno and others.

How to use it

1
Describe it in one phrase

For example “birthday photo”. Or paste a prompt you already wrote.

2
The prompt assembles itself

The type — image, text, music, video or code — is detected from the phrase.

3
Fill in the parts

Missing parts sit right inside the prompt text: tap one and fill it in.

4
Take it or test it

Copy the finished prompt — or run it here and see the answer.

Take a ready prompt for photos, chat or music — or build your own part by part

Prompt type
For example:

A pasted prompt is split into parts, and we show what is missing.

Ready-made prompts

Tap a card: fill in your own, copy it or open it in the builder. Whatever you type in the field above is matched here too.

Image, video and music prompts are given in English — that is the native language of those models.

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What it does

Prompt score 0–100
Shows which meaning-carrying parts a prompt has and which it lacks, and how much each one changes the result. Computed in the browser, instantly.
Testing on the browser’s model
Chrome 148 and Edge carry a model inside them: improving a prompt and running it are computed on your own device. Where there is no such model, the cloud path takes over.
The prompt is its own editor
Missing parts sit right inside the prompt text: tap any of them and fill it in. For chat that is role, task, format and constraints; for an image — light, angle and style; for music — genre, tempo and vocals.
Ready-made prompts with examples
A library by section: photos and photoshoots, birthdays, family, couples, kids, work, study, video, code, music. With variables you swap for your own.
Catches empty words
“Beautiful”, “high quality”, “modern” tell a model nothing. The tool highlights such words and asks for a visible property instead.
Knows model syntax
The --ar and --no flags are added only where they are understood. Image, video and music prompts are translated into English — the native language of those models.

Typical uses

  • Write a prompt for a photo session, a portrait, a kids' party or a family shot.
  • Take apart a prompt that "somehow does not work" and see what it is missing.
  • Take a ready prompt from the library and drop in your own name, age or product.
  • Assemble a precise brief for ChatGPT: role, output format and constraints.
  • Write a Suno or Sora prompt in English without knowing English.
  • Check two versions of a prompt and see the difference in the answers.

Why this one

A typical prompt generator hides the work inside a neural network and hands you a string: why it is better than yours is anyone’s guess, and there is nothing to check it with. Here it is the other way round. The structure is on show: which parts a prompt is made of, which you filled in, which are missing and how much each one weighs. Building, scoring and the library run in the browser. And testing — the thing almost nobody offers: some tools cannot run a prompt at all, others need your own model key. In browsers with a built-in model (Chrome 148, Edge) the test runs on your own device.

FAQ

How is this different from a generator that just rewrites my text?

It shows the structure. A typical generator hands back a finished string and never explains what changed or why. Here you see which parts a prompt consists of, which you filled in, which are missing and how much each affects the result. Someone else’s prompt works exactly the same way: paste it into the same field and the breakdown appears on its own — there is no separate place for it.

Are the prompts written for one particular model?

No, and that is deliberate: a prompt tuned to one model's quirks stops working the week that model is updated. What the score measures instead is whether the prompt carries the parts that any model needs to be told — the task, the role it should adopt, the audience, the format of the answer, the constraints, the examples. Those are properties of the request rather than of the system reading it, and a prompt that has all of them works better on every model than a prompt missing three of them works on the one it was written for. Where a model genuinely differs is in length limits and in whether it accepts images, and those are practical facts rather than phrasing tricks.

What is this “model in the browser”, and does everyone have it?

From Chrome 148 and recent Edge the browser carries a language model inside it: it downloads once, lives on disk and computes on your device. The tool probes for it and writes on the button itself which path a run will take. Firefox and Safari have no such model yet — there improve and test go through our neural network, while building, scoring and the library work the same everywhere.

Why are the image prompts written in English?

Because English is the native language of those models and they follow instructions in it more precisely. We measured this on our own music generator: a "no drums" instruction written in Russian was ignored in all six runs, while the English wording was obeyed in all six. So strengthening an image, video or music prompt returns English, while chat and code prompts come back in the language you wrote.

How is the score computed?

From the composition. Each kind of prompt has its own set of meaningful parts with its own weights: for an image, lighting weighs twice as much as palette, because lighting redefines the whole frame while palette only tints it. The score adds up the parts found, subtracts empty words, and cannot go above 40 without the main field.

It offers to run the prompt. Where does that run?

In the browser, on a model that recent versions of Chrome and Edge carry inside them — the model ships with the browser, so there is no key to paste and no account to open. That model is small and it is not the point: the value is in the loop rather than in the answer. Change one part of a prompt, run it, see what moved; add the missing audience, run it again, see the shape of the reply change. That is how you learn which parts of a request actually do work, and it costs nothing to repeat twenty times. What you should not do is judge a prompt's final quality here — a small local model and a large hosted one answer differently, and the prompt is being tested for structure, not for the reply it produced.

Are my prompts saved?

The prompts you build are kept in your browser's localStorage, so they survive a reload and you can come back to them instead of rebuilding. The button in the "Your prompts" section clears them.

Can I use these prompts for commercial work?

The prompts themselves are just text — instructions for a model — and we place no restrictions on using them. What you may do with the RESULT depends on the terms of the model you run it on: Midjourney, Suno, ChatGPT and the rest each set their own rules for commercial use.

Why is a music prompt two blocks?

Because Suno and Udio have two fields with different grammars. Style takes the description of the sound — genre, mood, instruments, tempo and key, vocal, exclusions — as one comma-separated line (up to 1000 characters on Suno). Song structure does not go there: it goes into the Lyrics field as [Intro], [Verse 1], [Chorus] tags above the lines. We tested on live Suno what actually matters: with a Russian-language style the model hit the requested key in one clip out of twelve, with English in 24 of 28, and it ignored a Russian “no drums” in all four clips. The separator makes no difference — commas and vertical bars “|” gave the same result — so the style is assembled with commas, as both models document.

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By uploading content you confirm that you are entitled to disclose its contents to third parties, and you undertake not to include other people's personal data in it. See the Terms of Use for details. AI providers Tembrica is an independent product. We are not affiliated with, endorsed by or sponsored by the makers of the models and services listed here; their names are used only to say factually which engine runs a given tool.

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