Add dataset card

README.md

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+---
+language:
+- en
+license: apache-2.0
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-generation
+- question-answering
+tags:
+- llm-evaluation
+- benchmarking
+- ai-safety
+- sft
+- synthetic
+- prompt-engineering
+- rlhf
+- hallucination
+- model-comparison
+pretty_name: LLM Evaluation SFT 100K
+---
+
+# LLM Evaluation SFT 100K
+
+A synthetic supervised fine-tuning dataset of 100,000 high-quality conversations covering LLM evaluation methodology, benchmarking, safety assessment, and prompt optimization. Designed to train AI assistants that can help ML engineers and researchers rigorously evaluate and improve language models.
+
+## Dataset Description
+
+This dataset covers the full spectrum of LLM evaluation practice across 7 specialized categories. Each record follows the ShareGPT format with a practitioner-level question and a detailed, methodology-rich response including working code examples.
+
+## Categories
+
+| Category | Description |
+|---|---|
+| `benchmark_design` | Designing reliable, contamination-free benchmarks |
+| `model_comparison` | Systematic model comparison, consistency measurement |
+| `hallucination_detection` | Measuring and reducing hallucinations in RAG systems |
+| `eval_frameworks` | LM Eval Harness, RAGAS, DeepEval, LangSmith comparison |
+| `prompt_optimization` | Systematic prompt engineering with statistical testing |
+| `safety_evaluation` | Red-teaming, bias testing, deployment safety gates |
+| `finetuning_evaluation` | Fine-tuned vs base model comparison, catastrophic forgetting |
+
+## Format
+
+ShareGPT format:
+```json
+{
+  "conversations": [
+    {"from": "human", "value": "...evaluation question..."},
+    {"from": "gpt", "value": "...methodology-rich response with code..."}
+  ],
+  "metadata": {"category": "...", "context": "..."},
+  "id": "uuid"
+}
+```
+
+## Use Cases
+
+- Fine-tuning AI assistants for ML evaluation tasks
+- Training models to reason about benchmark methodology
+- Building AI-assisted evaluation pipelines
+- Educating teams on LLM quality measurement
+- Safety evaluation tooling and red-teaming assistance
+
+## Quality Notes
+
+All responses include working Python code examples using popular evaluation frameworks (lm-eval, RAGAS, DeepEval, LangSmith), statistical testing methods, and production-ready evaluation patterns.