AI Eval Dataset Builder for fast browser-based work
Turn an AI task, pass criteria, and real failures into a maintainable JSONL eval dataset with judge focus notes.
中文：把 AI 任务、通过标准和真实失败案例整理成可维护的 JSONL eval 数据集，并生成评审重点。
Example: Use it when a prompt, chatbot, coding agent, or AI workflow keeps failing in subtle but repeated ways.
Where this tool fits in real work
Use cases
- Describe the AI task, pass criteria, and real good/bad examples.
- Generate JSONL cases that cover happy path, edge cases, ambiguity, privacy, format, and adversarial instructions.
- Copy a judge prompt that can be used for manual review or an eval runner.
Review notes
- This tool does not run model evaluations. It creates the dataset contract locally.
- Real repeated failures should become regression cases.
- Use placeholders instead of real users, secrets, accounts, or internal URLs.
Local-first handling
This page is built as a browser utility. Inputs are processed in the page where possible, with no account requirement and no intentional upload step for the tool workflow.
When to use AI Eval Dataset Builder
Good fit
- Describe the AI task, pass criteria, and real good/bad examples.
- Generate JSONL cases that cover happy path, edge cases, ambiguity, privacy, format, and adversarial instructions.
- Copy a judge prompt that can be used for manual review or an eval runner.
Before copying results
- This tool does not run model evaluations. It creates the dataset contract locally.
- Real repeated failures should become regression cases.
- Use placeholders instead of real users, secrets, accounts, or internal URLs.
Use a stricter workflow
If the context includes production secrets, customer records, private research material, or executable scripts, redact first and use a stricter human review workflow.
Keep learning this workflow
Keep working with nearby utilities
AI Eval Dataset Builder questions
Does it run the evaluation?
No. It builds a local dataset draft and judge prompt you can use manually or with an eval runner.
Why use real failures?
Real failures expose regressions that generic test cases miss.
Is this tool free?
Yes. The current Toolkits tools are free to use and do not require an account. If advertising is added later, it should be clearly labeled and kept away from primary tool controls.