Paste the AI prompt you want to generate test cases for.
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Configure options and click Generate// generate prompt testing scenarios in one click
Generate edge cases, normal cases, and failure scenarios for AI prompt evaluation. Build comprehensive test suites for LLM prompt engineering in seconds.
Paste the AI prompt you want to generate test cases for.
Ready to generate
Configure options and click GenerateEnter the AI prompt you want to test. Optionally add a system prompt or context.
Select which test categories to include: normal, edge, failure, adversarial, or multilingual.
Click Generate, review the test cases, then copy or download as JSON, CSV, or Markdown.
The Prompt Test Case Builder automatically generates structured test scenarios for AI prompts. Given a prompt, it produces normal-path cases, edge cases, failure cases, adversarial injection attempts, and multilingual inputs — giving you a complete evaluation suite with expected behaviors and testing notes.
The tool generates five categories: Normal cases (happy path, expected inputs), Edge cases (boundary conditions, unusual but valid inputs), Failure cases (invalid, missing, or malformed inputs), Adversarial cases (prompt injection and jailbreak attempts), and Multilingual cases (inputs in non-English languages).
The tool analyzes keywords in your prompt to classify it into a domain — coding, summarization, Q&A, translation, classification, creative writing, or general. Each domain has a curated bank of realistic test cases tailored to common challenges in that area.
Yes. You can export in three formats: JSON (structured, ideal for programmatic use), CSV (for spreadsheets and data pipelines), and Markdown (for documentation and pull request descriptions). Click Download after generation.
You can generate up to 10 cases per type, with up to 5 types selected simultaneously — meaning up to 50 total test cases in a single run. Each case includes an ID, input, expected behavior, and testing notes.
Adversarial test cases simulate attempts to manipulate the AI model — such as prompt injection attacks (hidden instructions in user input), jailbreak attempts (asking the model to ignore its guidelines), or false history injection (claiming the model agreed to something it didn't). These are critical for safety evaluation.
No. This tool is completely free and browser-based. No sign-up, no account, no data collection. Everything runs server-side in PHP and the results are returned directly to your browser.
Paste your LLM's system prompt or any persistent context here. For example: "You are a customer support bot for Acme Corp. Always respond in formal English." This helps you think about how your test inputs interact with the full prompt configuration.
Yes. The generated cases — especially the expected behavior and notes fields — can serve as the basis for creating labeled training data. Export to JSON or CSV and use the input/expected_behavior pairs as your dataset structure. You'll want to run actual model outputs to complete the dataset.
A Prompt Test Case Builder is a tool that automatically generates structured test scenarios for AI prompts and language model applications. When you build a product powered by a large language model (LLM) — whether a chatbot, document summarizer, code assistant, or question-answering system — you need to verify that your prompt behaves correctly across a wide range of inputs. The Prompt Test Case Builder does this automatically, giving you a comprehensive evaluation suite in seconds.
Rather than manually brainstorming dozens of test inputs, this tool analyzes your prompt, detects its domain (coding, summarization, Q&A, etc.), and generates categorized test cases covering normal usage, edge cases, failure scenarios, adversarial attacks, and multilingual inputs — each with expected behaviors and testing notes you can hand directly to a QA team or automated evaluation pipeline.
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Prompt engineering is one of the most critical — and underestimated — disciplines in modern AI development. A prompt that works perfectly for a typical user input might completely fail when given an empty string, an input in a different language, a contradictory instruction, or a carefully crafted adversarial attack. Without systematic testing, these failure modes only surface in production, where they damage user trust and product reputation.
Systematic prompt testing addresses several key risks:
Normal cases represent the happy path — valid, well-formed inputs that a typical user would provide. These cases verify that your prompt's core functionality works as intended. They are your baseline. Every prompt should pass all normal cases before you worry about anything else. Normal cases reveal basic issues like missing instructions, overly vague guidance, or incorrect output format specifications.
Edge cases sit at the boundaries of expected input — not wrong, but unusual. Empty strings, single-word inputs, extremely long documents, inputs with conflicting constraints, or inputs that technically satisfy the prompt requirements while being unusual in practice. Edge cases are where most prompt failures live, because they expose assumptions baked into the prompt that the developer didn't realize they were making.
Failure cases are definitively invalid inputs: garbage text, missing required fields, requests for harmful content, or inputs that violate the fundamental premise of the prompt. A well-designed prompt and model should handle these gracefully — returning a clear error, asking for clarification, or declining politely — rather than hallucinating a response or crashing silently.
Adversarial cases simulate deliberate attacks on your prompt system. This includes prompt injection (embedding hidden instructions in user input to override your system prompt), jailbreak attempts (asking the model to "pretend" it has no guidelines), and false history injection (claiming the model agreed to something in a previous conversation turn). For any AI product deployed to real users, adversarial testing is not optional.
Multilingual cases test how your prompt handles inputs in languages other than the one it was written in. Modern LLMs understand dozens of languages, but your prompt instructions may interact unexpectedly with non-English inputs. Does the model respond in the user's language? Does it translate before processing? Does it fail on right-to-left scripts? Multilingual testing answers these questions before they become support tickets.
The Prompt Test Case Builder is the first step in a full evaluation pipeline. Once you have your test cases, the workflow looks like this:
Different types of AI applications have different failure modes, which is why the Prompt Test Case Builder detects your prompt's domain and generates domain-specific test cases:
Coding prompts need tests for syntax errors, unknown languages, empty input, performance edge cases (large inputs), and security-sensitive requests. A code assistant should never help write malware, even if asked cleverly.
Summarization prompts need tests for empty input, single-word input, contradictory content, extremely long documents, and multilingual source text. The model should not add information that wasn't in the original document (hallucination risk).
Q&A prompts need tests for unanswerable questions, questions with false premises, subjective questions, and questions requiring real-time data the model doesn't have. A good Q&A prompt instructs the model to say "I don't know" rather than making up an answer.
Classification prompts need tests for ambiguous inputs that could belong to multiple categories, inputs that clearly belong to no category, and adversarial inputs designed to game the classification system.
Use the test cases generated here as a starting point, not a complete substitute for domain-specific testing. Add your own test cases based on real user feedback and edge cases you encounter in production. Document expected behaviors clearly — the more specific your expected behavior description, the easier it is to evaluate automatically. Treat your prompt like production code: version it, test it, and review changes before deploying.