LLM Data Labeling and RLHF Annotation Services for Model Fine Tuning and Evaluation

Large language models rely on high quality supervised data and human feedback to improve alignment, reasoning, safety, and task performance. Fine tuning LLMs requires structured datasets built from detailed human judgments including preference ranking, response scoring, critique generation, and safety evaluation.DataVLab provides LLM data labeling services designed for teams developing advanced generative AI systems. We support supervised fine tuning, RLHF, RLAIF assisted labeling, reward model training, and continuous evaluation workflows. Our annotators follow detailed guidelines to assess helpfulness, relevance, factuality, tone, safety compliance, and domain specific correctness.We evaluate model responses across multiple difficulty levels including step by step reasoning, summarization, instruction following, task completion, and domain based question answering. Our workflows include multi pass review, calibration rounds, annotation adjudication, and guideline refinement to maintain consistency.For sensitive datasets or compliance heavy projects, we offer EU based annotation teams and secure infrastructure. We also support domain level labeling for healthcare, finance, insurance, legal services, and technical content, ensuring that specialized LLMs receive accurate and context grounded annotations.These workflows help teams improve model alignment, reduce hallucinations, and produce fine tuned models that behave reliably in enterprise environments.