Prompt Dataset Builder

The problem

Building high-quality LLM evaluation suites or fine-tuning datasets is slow and prone to bias. Developers often rely on repetitive prompts that fail to cover edge cases, leading to models that break in production when faced with adversarial inputs.

What it does

• Constructs a balanced task taxonomy including summarization, function calling, and RAG.
• Generates diverse prompt seeds across easy, medium, and hard difficulty levels.
• Creates systematic negative cases, including ambiguous instructions and contradictory facts.
• Enforces a consistent metadata schema for every row to support automated evaluation pipelines.
• Performs de-duplication to ensure dataset variety and category balance.

Why this beats prompting it yourself

Manual generation usually results in a narrow distribution of examples that lacks boundary testing. This skill applies a structured matrix approach to ensure you hit edge cases like unicode handling, maximum token counts, and policy-adjacent refusals that a single-prompt approach misses.

Use cases

• Building a gold-standard evaluation set for a new RAG feature.
• Generating diverse SFT or DPO training seeds for specialized model fine-tuning.
• Red-teaming assistant behavior for safety and refusal benchmarking.
• Expanding a small set of user queries into a comprehensive testing suite.

Known limitations

This skill generates the dataset contents only. It does not provide the execution runner, statistical analysis tools, or embedding-based cosine similarity logic.