Paste the prompt you want to QA-check
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Paste your prompt and click Generate Checklist// create prompt QA checklists in one click
Generate comprehensive prompt QA checklists covering safety, edge cases, formatting, and fallback behavior. Free, browser-based, no sign-up required.
Ready to generate
Paste your prompt and click Generate ChecklistEnter the system prompt or instruction set you want to quality-check.
Choose which QA dimensions matter most — safety, edge cases, formatting, fallback, tone, or scope.
Click Generate and copy the Markdown checklist or download it as a .md file.
The Prompt Checklist Generator analyzes your LLM prompt and produces a structured QA checklist. It surfaces potential blind spots across six critical dimensions: safety guardrails, edge cases, output formatting, fallback behavior, tone consistency, and scope clarity — so you can ship prompts with confidence.
No. All checklist generation happens entirely in your browser using JavaScript. Your prompt text never leaves your device. No data is stored or transmitted.
Safety checks verify that your prompt addresses potential misuse, harmful content, jailbreak resistance, PII handling, and whether the model has clear refusal instructions for out-of-scope requests.
Edge cases include handling empty input, very long messages, non-English text, ambiguous queries, conflicting instructions, and adversarial inputs that could break expected behavior.
Yes — the checklist is model-agnostic and works for any LLM including OpenAI GPT models, Anthropic Claude, Google Gemini, Mistral, Llama, and custom fine-tuned models.
Fallback behavior covers what the model should do when it cannot answer — does it gracefully decline, ask for clarification, redirect to a human, or provide a default response? Good prompts define this explicitly.
Yes. You can copy the checklist as Markdown with one click, or download it as a .md file to include in your project documentation or team review workflow.
A Prompt Checklist Generator is a specialized QA tool that analyzes your LLM (Large Language Model) prompt and surfaces a structured list of quality checks across critical dimensions — safety, edge cases, formatting, fallback behavior, tone, and scope. Instead of shipping prompts and discovering problems in production, you run them through a checklist first.
Think of it like a linter for your AI instructions. Just as ESLint catches JavaScript bugs before runtime, a prompt QA checklist catches instruction gaps before your users do.
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As AI-powered features become a core part of software products, prompt engineering is now a serious discipline. A poorly written prompt can cause:
A systematic checklist forces you to think through each dimension before deployment — not after.
Safety checks are the most critical. You need to verify: Does the prompt define what the model should refuse? Does it handle requests for harmful content (violence, self-harm, illegal activity)? Is there guidance on PII (personally identifiable information) handling? Does the prompt resist prompt injection — where a user tries to override your instructions?
Example safety check: "Does the prompt explicitly instruct the model to decline requests for personal data or medical advice?"
Edge cases are the situations you didn't design for. Classic examples: what happens when the user sends an empty message? A 10,000-word essay? A message in Japanese when your prompt is English-only? A string of emojis? A SQL injection attempt? A prompt in a different character encoding?
Good prompts anticipate these and provide graceful handling. Our checklist surfaces the ones most relevant to your specific prompt.
Output formatting issues are a leading cause of integration bugs. If your application expects JSON, does the prompt guarantee JSON — or could the model return a conversational preamble before the JSON block? Does it specify whether to use markdown? How long should responses be? Is there a maximum token guideline?
Formatting checks prevent schema mismatches that break your application silently.
Every prompt needs a fallback strategy. When the model genuinely cannot answer — because the topic is outside scope, the question is ambiguous, or information is unavailable — what should it do? Options include: ask a clarifying question, redirect to a human agent, return a default "I don't know" response, or provide partial information with caveats.
Undefined fallback behavior leads to hallucinations, where the model invents an answer rather than admitting uncertainty.
Tone consistency matters enormously in user-facing products. Is the model always formal? Always casual? Does it use "you" or "the user"? Does it use humor, and if so, when is that appropriate? Is it empathetic in sensitive contexts? Does it maintain persona consistently across a long conversation?
Tone checks ensure your AI feature feels like a coherent product, not a random response generator.
Scope checks answer: does the prompt clearly define what the model should and should not do? A customer support bot should not give legal advice. A coding assistant should not generate content unrelated to programming. Clear scope boundaries improve reliability and reduce the attack surface for prompt injection.
Here's a practical workflow for using prompt checklists in a team setting:
.md file and commit it alongside your prompt in version controlBeyond QA, here are proven best practices for writing prompts that hold up in production:
These are related but distinct activities. Prompt QA (what this tool helps with) is a static analysis — reviewing the prompt text before running it. Prompt testing is dynamic — actually running the prompt against an LLM with a test suite of inputs and evaluating the outputs.
Both are necessary. QA catches structural issues in the prompt itself. Testing catches behavioral issues that only appear at runtime. Use this checklist generator for QA, then follow up with automated evals using tools like PromptFoo, Braintrust, or LangSmith.
Prompt checklists are useful for anyone who ships AI-powered features: prompt engineers who write and maintain system prompts professionally, software engineers integrating LLM APIs into applications, product managers defining AI feature behavior, QA engineers adding AI to their test coverage, and AI safety researchers conducting red-teaming exercises.
Whether you're building a simple chatbot or a complex multi-agent system, a structured review process pays dividends in reliability and safety.