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:
{
"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.