RLHF & Human Feedback
Expert human evaluators providing preference rankings, safety assessments, and alignment feedback that guide AI models toward helpful, honest, and harmless behavior.
Aligning AI With Human Values
Reinforcement Learning from Human Feedback is the process that transforms capable language models into safe, useful assistants. It requires skilled human evaluators who can consistently judge response quality, identify subtle safety violations, and provide the preference signal that reward models learn from. Our RLHF teams include trained evaluators, domain experts, and safety specialists who understand the nuances of AI alignment — from detecting hallucinations and reasoning errors to identifying cultural biases and harmful content generation.
- Pairwise and listwise preference ranking
- Safety evaluation and red teaming
- Reward model training data generation
- Hallucination detection and factual grounding
- Multi-dimensional scoring (helpfulness, safety, honesty)
Human Feedback Services
Comprehensive evaluation and feedback workflows for every stage of the AI alignment process.
Preference Ranking
Side-by-side comparison of model outputs where evaluators rank responses on helpfulness, accuracy, completeness, and style. Produces the preference pairs that power DPO, PPO, and reward model training for model alignment.
Safety Evaluation
Systematic assessment of model outputs for harmful content, bias, misinformation, and policy violations. Our safety teams evaluate responses against your safety taxonomy and flag content requiring model correction or refusal training.
Red Teaming
Adversarial prompt crafting to discover model vulnerabilities, jailbreak vectors, and safety boundary failures. Our red team specialists probe models with creative attacks to identify weaknesses before they reach production users.
Hallucination Detection
Factual verification of model claims against source documents and world knowledge. Evaluators identify fabricated facts, unsupported claims, and reasoning errors to produce training signal that reduces model confabulation.
Multi-Dimensional Scoring
Rubric-based evaluation across multiple quality dimensions — helpfulness, harmlessness, honesty, instruction following, and format compliance. Provides rich, structured feedback that goes beyond simple preference comparisons.
Conversational QA
Human evaluation of multi-turn conversation quality — coherence, context retention, appropriate escalation, and graceful handling of out-of-scope requests. Critical for validating chat model behavior before deployment.
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LLM Training Data
Instruction datasets and fine-tuning corpora crafted by domain experts for language model training.
Learn moreAI Model Evaluation
Comprehensive benchmarking, bias detection, and performance analysis for production AI systems.
Learn moreHuman-in-the-Loop AI
Continuous human feedback loops for active learning, verification, and model improvement.
Learn moreAlign Your AI With Expert Human Judgment
Get a sample evaluation of your model's outputs with detailed preference rankings, safety assessments, and quality scores — free pilot, no commitment required.