AI Prompt Optimizer

What is the AI Prompt Optimizer?

The AI Prompt Optimizer is a free tool that rewrites your existing prompt onto a framework you choose, such as RACE, RISE, SPARK or chain of thought. It keeps the intent, moves what your prompt already says into the framework's parts, and fills in any part your prompt left out with a default you can adjust.

How to use the AI Prompt Optimizer

1. Paste the prompt you already use into Prompt to rewrite.
2. Pick a framework under Rewrite onto: one of the ten named frameworks, or Chain of thought.
3. Press Optimize prompt.
4. Read the Optimized Result, starting with anything your original never mentioned, then copy it or press Open in ChatGPT or Open in Claude. The first time, sign up free to reveal it.

What does a prompt optimizer change?

Three things. It names parts the original left implicit, it lifts standing rules out of the task sentence, and it turns adjectives into specifications: "short" becomes a length, "formal" becomes a described register, "a list" becomes a stated number of items in a stated shape.

Where the framework asks for something your prompt never said, the optimizer fills it in. Read those parts first and swap in the specifics only you know.

How do you know the optimized prompt is better?

Not by reading it. A rewrite looks better because it is longer and more organised, and neither is evidence. Run both versions on the same two or three inputs and compare the outputs against what you wanted.

The measure that matters is agreement across runs. Run each version three times. If the original gives three differently structured answers and the rewrite gives three that differ only in wording, the rewrite is doing its job. For a quick review before you test, the AI prompt checker scores a prompt out of 10 and lists the techniques it misses.

When should you not optimize a prompt?

When it already works. When its output is wrong because the model lacks a fact, since no structure supplies an absent fact. And when you run it at volume without watching the bill: the rewrite is usually longer, which is a real per-call cost in an API loop rather than a chat window.

How do you optimize your AI prompts?

Optimize an AI prompt by deciding what a good answer looks like, testing the prompt on fixed inputs, and changing one thing at a time. Without a description of the good answer, any rewrite is a matter of taste.

A workable routine: collect three real inputs the prompt will meet, including an awkward one. Run the current prompt on all three and note what is wrong with each output. Rewrite to fix the most common fault, run all three again, and keep whichever version wins. Save every version with a date, because a later change sometimes breaks something an earlier one fixed.

This tool does the rewrite step. It does not run your prompt or judge the outputs, so the testing is yours.

Keep the test inputs fixed across versions. If you change the input and the prompt together, you cannot tell which change moved the output. Judge each output against the description of a good answer you wrote first, not against the other version's output.

The fastest wins usually come from three edits: stating the output format exactly, adding one example of a good answer, and adding a rule for missing information. Try those before anything more elaborate. Length is a cost, not a goal: if a shorter version does as well on your inputs, keep the shorter one.

Which AI is best for prompt optimization?

The best AI for prompt optimization is the one that will run the final prompt, used as the test, not as the writer. Any current model can restructure a prompt; only the target model can show whether the restructured version works on it.

This optimizer uses an OpenAI model to do the rewrite, and the output is plain text that runs anywhere. Provider-specific details are not part of the rewrite: if the prompt will run on Claude, consider moving its parts into XML tags; if it will run through an API, move the standing rules into the system message and keep the task in the user message.

You can also use a model as a reviewer. Paste the prompt and one bad output, and ask what in the prompt allowed that output. That question is more useful than "improve this prompt", because it points at a specific sentence.

Optimizing for a reasoning model is mostly subtraction: cut step-by-step instructions the model would follow anyway, and keep the goal, the constraints and the output format.

What is the difference between a prompt optimizer and a prompt generator?

A prompt optimizer starts from a prompt you already have; a prompt generator starts from a goal. The optimizer keeps your intent and wording where it can and reorganises it, so it suits prompts that mostly work but give uneven results. A generator writes from scratch, so it suits a new task.

If you do not have a draft yet, the best AI prompt generator builds one on the same ten frameworks from a plain goal. Many people use both: generate a first version, use it for a while, then optimize it once they know where it falls short.

There is also a difference in risk. A generator can only miss what you did not say. An optimizer can also change what you did say, by merging two instructions or softening a constraint, so compare the rewrite with your original line by line before you replace it.

Does OpenAI have its own prompt optimizer?

Yes. OpenAI's developer dashboard has a prompt optimizer for API users that rewrites a prompt to its current best practices. OpenAI's own documentation says to review and test an optimized prompt before using it, because it can perform worse than the original on some inputs.

That advice applies here too. This page needs no OpenAI account or API dashboard, and it rewrites onto a named framework you choose, so you can see exactly which parts were added. The rule for both is the same: a rewrite is a candidate until it beats the original on your inputs.

Other prompt optimizers exist too, including open-source projects and browser extensions that sit inside the chat window. They do the same basic job: restructure, name the parts, fill the gaps. The useful difference between them is whether they show you what changed and whether they help you test the result.

Whichever optimizer you use, keep your original prompt. If the rewrite does worse on an input that matters, you want to be able to go back to the version that worked, not reconstruct it from memory.