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Synthesize golden LLM evaluation benchmarks, Ragas ground truth datasets, adversarial edge case prompts, and fine-tuning JSONL pairs directly from documentation or code with 100% browser-local privacy.
Build golden evaluation benchmarks, Ragas ground truth datasets, and fine-tuning pairs in browser memory.
Input raw API documentation, legal contracts, customer support guidelines, or source code. Choose pre-loaded domain templates for instant scenario setup.
Set target sample counts, toggle adversarial prompt injection edge cases, and include unanswerable anti-hallucination tests to verify refusal boundaries.
Review ground truth answers, edit prompt variations inline, estimate eval token costs, and export to JSONL, Ragas JSON, CSV, or Vitest code.
Zero backend network calls. Proprietary code, documentation, ground truth Q&A pairs, and exported datasets are processed 100% in local browser memory.
Generate Ragas ground truth JSON schemas, OpenAI messages fine-tuning JSONL files, multi-turn agent dialogues, and intent classification rubrics.
Injects prompt injection test pairs, unanswerable queries, and anti-hallucination refusal prompts to verify model guardrail boundaries.
Filter by difficulty (Easy, Medium, Hard, Edge Case), edit questions and ground truth answers in-place, and customize evaluation tags.
Calculates total token usage and estimates evaluation execution costs across GPT-4o, Claude 3.7 Sonnet, DeepSeek R1, and Gemini Flash.
Export datasets to JSONL, Ragas JSON, CSV spreadsheets, or Vitest/Jest unit test integration files with one click.
As AI applications move from prototype to production in 2026, Evaluation-Driven Development (EDD) has replaced ad-hoc prompt testing. High-performing engineering teams rely on curated "golden datasets" and multi-metric rubrics (Ragas Faithfulness, Context Recall, Answer Relevancy) to catch regressions before shipping model updates or system prompt modifications.
By exporting synthetic test suites directly to standard OpenAI JSONL or Vitest integration files, developers can easily integrate continuous model evaluation into GitHub Actions pipelines.
HiMat's AI Evaluation Dataset Generator operates strictly in your browser memory. Proprietary code, internal documentation, ground truth Q&A pairs, and exported datasets never touch external servers or third-party APIs.
It is a browser-local developer utility that generates golden evaluation benchmarks, Ragas ground truth test pairs, and fine-tuning datasets from raw documentation or code to measure LLM accuracy, faithfulness, and refusal safety.
Ragas is a standard framework for evaluating Retrieval-Augmented Generation (RAG) pipelines. Ragas ground truth datasets pair questions and context chunks with verified answers to compute Faithfulness, Context Recall, and Answer Relevancy metrics.
No. The HiMat AI Evaluation Dataset Generator operates 100% locally in your web browser. Neither your text context, ground truth answers, nor exported test files leave your device.
Yes! The tool generates ready-to-use Vitest/Jest test code, OpenAI Evals JSONL files, and CSV spreadsheets for seamless integration into GitHub Actions CI pipelines.
HiMat Technologies builds eval-gated AI agents, fine-tuned domain LLMs, and enterprise RAG systems with rigorous quality metrics for high-growth tech platforms.
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