LLM Training Data
Human-crafted instruction datasets, fine-tuning corpora, and preference pairs that make large language models more helpful, accurate, and aligned with your domain.
The Data Behind Better Language Models
Large language models gain their capabilities during pre-training, but they gain their usefulness during fine-tuning. We produce the high-quality supervised fine-tuning (SFT) datasets, instruction-response pairs, and preference data that transform base models into production-ready AI assistants. Our writers and domain experts craft training examples that teach models to follow instructions precisely, reason through complex problems, write in specific styles, and respond safely to sensitive queries — all calibrated to your product's voice and requirements.
- Instruction-response pairs for supervised fine-tuning
- Preference pairs (chosen/rejected) for DPO and RLHF
- Multi-turn conversation data for chat models
- Domain-specific corpora (legal, medical, financial, technical)
- Red teaming and safety evaluation datasets
LLM Data Services
Every type of training data your language model pipeline requires, from pre-training to alignment.
Instruction Datasets
Expert-written prompt-response pairs covering reasoning, summarization, code generation, creative writing, and domain-specific tasks. Each example is crafted to demonstrate the behavior you want your fine-tuned model to exhibit.
Preference Data
Side-by-side response comparisons with human rankings for Direct Preference Optimization (DPO) and RLHF reward modeling. Annotators evaluate helpfulness, accuracy, safety, and style to produce the preference signal that aligns models.
Conversation Data
Multi-turn dialogue datasets for chat model training. We create realistic conversation flows with context carryover, clarification handling, tool use, and graceful failure modes that teach models to hold natural, productive conversations.
Domain Corpora
Specialized training data written by subject matter experts in healthcare, law, finance, engineering, and science. These corpora inject domain knowledge and terminology that base models lack for specialized enterprise applications.
Safety & Alignment Data
Red teaming prompts, safety refusal examples, and boundary-testing scenarios that teach models to decline harmful requests while remaining helpful. Includes adversarial prompt engineering and jailbreak resistance training data.
Evaluation Sets
Expert-curated test sets for measuring model capabilities across dimensions: factual accuracy, reasoning depth, instruction following, format compliance, and safety. Designed to surface model weaknesses before deployment.
Frequently Asked Questions
Explore More Services
RLHF & Human Feedback
Preference ranking, safety evaluation, and alignment data for reinforcement learning from human feedback.
Learn moreAI Model Evaluation
Benchmarking, red teaming, and bias detection to validate model performance before deployment.
Learn moreText & NLP Annotation
Named entity recognition, sentiment analysis, and classification for NLP model training.
Learn moreFine-Tune With Data That Makes a Difference
Tell us your model, domain, and target behavior. We'll produce a sample dataset and demonstrate the quality that sets our training data apart.