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
Turn rough instructions into clearer, testable prompts for ChatGPT, Claude, Gemini, and other AI models. Paste a prompt below to start without signing in.
Start without signing in Review changes before copying Built for ChatGPT, Claude, and Gemini
One workflow, multiple model contexts
ChatGPTClaudeGeminiOpenAI-compatible APIsPrompt QA workflows
What the optimizer checks
Improve the decision structure, not just the word count
A useful prompt makes the task, context, constraints, and expected output visible. Prompt Optimizer turns those hidden decisions into a revision you can inspect and test.
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 only the context it needs
“Make this better” leaves the model to guess what success means. A reviewable prompt names the audience, evidence boundary, format, and fallback behavior. The optimizer surfaces those decisions while flagging assumptions that still need your judgment.
A proposed rewrite is a starting point. Test important prompts with representative inputs before using them in production.
Model-specific examples
The same goal needs different context for each model
Use the target model as a review lens, not as a promise of official integration or identical behavior across model versions.
ChatGPT
ChatGPT prompt example
Rough prompt
Write a launch email for our new analytics feature.
Optimized prompt
You are a product marketing writer. Context: the feature helps small teams spot weekly trends. Task: write a launch email for [audience]. Constraints: 120-160 words, plain language, no unsupported claims. Output: one subject line, then the email body with one CTA.
Why it works better: ChatGPT gets a clear role, bounded context, and predictable structure, so the result is easier to review and edit.
Claude
Claude prompt example
Rough prompt
Review this policy and tell me what to fix.
Optimized prompt
Act as a careful policy reviewer. Context: text inside <policy> tags is authoritative; do not invent requirements. Task: find unclear, conflicting, or risky language. Constraints: quote the evidence, separate facts from assumptions, and suggest a concise revision. Output: a Markdown table with Issue, Evidence, Risk, and Revision.
Why it works better: Explicit section boundaries and evidence-first output give Claude a long-context review it can trace back to the source.
Gemini
Gemini prompt optimization example
Rough prompt
Research the best laptop for a small design team.
Optimized prompt
You are a research analyst. Context: compare laptops for a 5-person design team using the supplied sources and product links. Constraints: use information current to [date], separate sourced facts from estimates, flag missing data, and stay within [budget]. Output: a Markdown table with Model, Price, Design fit, Source, and Caveat, followed by a 3-sentence recommendation.
Why it works better: Use our gemini prompt optimizer workflow for this source-aware structure: Gemini has a clear research scope, source policy, and schema to follow.
A repeatable workflow
Move from rough instruction to testable prompt
Good prompt optimization is an iteration loop: describe the work, refine the instruction, then verify whether the change helps on the inputs that matter.
Paste
Paste a rough instruction into the editor, then add any audience, source material, or example that should shape the answer. Starting with the actual task gives the optimizer enough context to preserve your intent instead of polishing a vague sentence into something unusable.
Shape
Choose a prompt type, target model, and optimization goal. Describe constraints such as length, tone, required fields, variables, citations, or fallback behavior. These settings turn hidden assumptions into decisions you can inspect, edit, and carry into a repeatable workflow.
Compare
Compare the original with the proposed prompt, then review the change summary, assumptions, and risks. Keep the version that performs better on representative examples; use the result as a starting point, not as an automatic guarantee of better model output.
What to test after the rewrite
Use one normal input, one boundary case, and one input that should trigger a safe fallback. A result that looks better on one example can still regress on the edge case that matters most.
Common prompt patterns
Make recurring AI work easier to reuse
Prompt structure depends on the work. Use these patterns to give the editor enough signal to preserve intent, expose missing decisions, and produce an output you can review.
Writing and content
Turn a broad request into a brief with audience, tone, length, source boundaries, and a defined format. Separate the stable writing instruction from variables such as product, language, or campaign details so the same prompt can support emails, briefs, summaries, and editing.
Coding and debugging
Make the task, repository context, constraints, and acceptance checks explicit before asking for code. Add expected files, error output, compatibility requirements, and the test command so the model has fewer decisions to guess and the result is easier to review against a real implementation.
Research and analysis
Define the question, evidence boundary, time range, source policy, and uncertainty handling. A stronger research prompt tells the model how to distinguish facts from assumptions, cite supporting material, and format findings so you can check the reasoning instead of trusting a confident paragraph.
Data and structured output
Describe input fields, data types, missing-value behavior, and the exact output schema. This helps turn invoices, support tickets, or survey text into consistent JSON or tables, while validation rules make incomplete or ambiguous records visible before they reach a downstream workflow.
Customer support and operations
Set the agent role, escalation rules, tone, account-data boundaries, and response categories. A stronger support prompt includes safe fallbacks for missing details and makes clear when a request should be handed to a person rather than answered with a guess.
Role-play and business
Give the role, objective, audience, decision criteria, and limits of the simulation. For interviews, sales objection practice, strategy workshops, or stakeholder reviews, explicit turn-taking and feedback rules make the exercise more realistic, repeatable, and easier to assess.
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.
Need a repeatable way to revise and verify an instruction? Read our six-step prompt optimization guide.
Ready to judge whether a revision actually helps? Read our prompt evaluation guide.
See workspace plans Version history, reusable test cases, and multi-model evaluation are planned workspace features, separate from the current credit balance.
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.
Keep going
Make your next prompt easier to trust
Start free, review every change, and move into a repeatable workflow when prompt quality becomes part of the product.