Clearer task definition
Surface the goal, audience, context, and missing details before the rewrite adds more words.
Prompt quality, made reviewable
Free AI Prompt Optimizer for ChatGPT, Claude & Gemini
Turn a rough instruction into a clearer, more testable prompt. Start with a free optimization, then save versions and test the result when the work matters.
The free run is not saved to a workspace. Review every proposed change before using it with real data.
Prompt workspace
Optimize a prompt you can actually test
Give the optimizer your rough instruction, its job type, target model, and the improvement that matters most.
Free runs are not saved to a workspace. Do not paste API keys, passwords, or confidential personal data.
Your optimized prompt will appear here
The result includes a clearer instruction, a change summary, assumptions, and risks to review.
Goal Context Constraints Format
What the optimizer checks
A prompt optimizer should improve decisions, not just add words
A useful prompt is specific enough to guide a model and flexible enough to handle the inputs your workflow actually produces. The optimizer looks for the parts that make a prompt testable and reusable.
Explicit constraints
Turn vague preferences into reviewable boundaries for tone, length, sources, safety, and output behavior.
Reusable variables
Keep changing values such as audience, product, language, or input text separate from the stable instruction.
Visible changes
Read a concise change summary and quality notes so you can keep, edit, or reject the proposed rewrite.
Start with the task, then add the necessary context
Many weak prompts begin with a desired tone or a vague request such as “make this better.” The model still has to guess what success means, who will read the result, what source material is authoritative, and how the answer should be formatted. A better prompt begins with the task and makes those decisions visible. This is useful for a one-off ChatGPT question, a Claude system instruction, a Gemini research workflow, or a reusable prompt inside an application.
Keep constraints concrete
“Be concise” can mean one paragraph, five bullets, or a short answer with no explanation. A reviewable prompt turns that preference into a constraint the user can inspect: a word range, a number of bullets, a required field list, a citation rule, or an explicit fallback when the source does not contain the answer. The optimizer suggests these structures when the input supports them, while marking assumptions that still need your decision.
A proposed rewrite is a starting point. It can make an instruction clearer while also making an incorrect assumption, so important prompts still need representative tests.
A repeatable workflow
How to use a prompt optimizer
Good prompt optimization is an iteration loop: describe the work, refine the instruction, then verify whether the change helps on the inputs that matter.
Describe
Paste the rough instruction and add any audience, source, or constraint that should shape the answer.
Refine
Choose the prompt type, target model, and optimization goal so the rewrite has a clear direction.
Verify
Review the optimized prompt, changes, assumptions, and risks before you use it in a real workflow.
What to test after the rewrite
Use at least one normal input, one boundary case, and one input that should trigger a safe fallback. For a support prompt, that might mean a clear question, an incomplete account detail, and a request that should be escalated. For a coding prompt, include a small successful change, an ambiguous requirement, and a failing test. The future workspace will make these examples reusable, but you can already compare the original and optimized text above.
Model-aware prompts
Choose a target without locking the prompt to one vendor
Model presets help the optimizer understand the context in which the prompt will be used. They do not represent an official integration or guarantee identical behavior across model versions.
| Target | Useful emphasis | Review before use |
|---|---|---|
| Any model | Portable instructions, explicit inputs, and a stable output format. | Different models may follow priorities and examples differently. |
| ChatGPT | Clear user task, context boundaries, tools, and structured output. | System/developer instructions and tool permissions are configured where you run it. |
| Claude | Long-form context, role separation, XML-like sections, and careful constraints. | Keep system instructions separate from the user's changing input. |
| Gemini | Research scope, source handling, multimodal context, and response schema. | Check grounding, citations, and behavior for long or mixed inputs. |
For general prompt design background, compare the guidance in the OpenAI prompt engineering documentation with the requirements of the model and product you actually use.
From rewrite to QA
Why prompt evaluation and versioning belong together
Optimization produces a candidate. Evaluation tells you whether it works on representative inputs. Versioning lets you identify the candidate, compare it with the previous prompt, and roll back when the output regresses.
Define a small rubric
Choose criteria that match the task, such as factual support, required fields, tone, refusal behavior, or code test success. Avoid using a single generic score as a substitute for judgment.
Run the same examples
Use the same inputs for the original and optimized prompt. A result that looks better on one example may fail on an edge case or become unnecessarily expensive on a longer context.
Keep the decision visible
Record what changed, which model ran, what the test covered, and why a version was accepted. This is the foundation for a prompt library that a team can maintain.
See workspace plans Version history, reusable test cases, and multi-model evaluation are paid workflow features.
Questions answered
Prompt optimizer FAQ
Understand what the first version can do, what it sends to a model provider, and why the result still needs review.
What does a prompt optimizer do?
A prompt optimizer reviews an instruction for missing context, unclear constraints, conflicting requirements, and ambiguous output formats. It then proposes a clearer prompt that you can review and test instead of treating a longer prompt as automatically better.
Can I use this prompt optimizer for ChatGPT?
Yes. Select ChatGPT as the target model when the prompt will be used there. The optimizer keeps the result as portable text and does not claim an official relationship with OpenAI.
Can I optimize a Claude prompt?
Yes. Choose Claude to make the target context explicit. Review the distinction between system instructions, user input, examples, and output requirements before moving the result into Claude.
What is the difference between a prompt optimizer and a prompt enhancer?
An enhancer often focuses on rewriting or expanding wording. An optimizer should also identify the goal, context, constraints, variables, and testable output format. This tool shows the proposed changes so you can decide which ones to keep.
Does the prompt optimizer store my free prompt?
Free optimization requests are not saved to a workspace by the website. The prompt is sent to the configured model provider to produce the result, so do not paste API keys, passwords, or confidential personal data. The privacy policy explains technical logs and provider processing.
Can prompt optimization guarantee a better answer?
No. A rewrite can improve clarity while changing an assumption or making a prompt too rigid. Use the change notes and test cases to compare the original and optimized versions on representative inputs.
What is prompt versioning useful for?
Prompt versioning keeps each revision identifiable and recoverable. It helps you compare changes, connect a prompt to test cases, and roll back when a new version causes an output regression.
How many free prompt optimizations can I run?
Anonymous visitors can run up to three standard optimizations per day within the displayed input limit. Signed-in free accounts receive a larger daily allowance. Paid credits are used for higher-volume optimization and evaluation workflows.