AI Data Services / GenAI Data For Model Training & Fine-Tuning
Model Training & Fine-Tuning
Take your LLM to the next level.
Your GenAI model should be free from harmful biases and hallucinations.
Our GenAI model training includes supervised fine-tuning (SFT), which means taking a pre-trained model and adapting it to a specific task using annotated data. With curated datasets we align your GenAI more closely to your desired behaviors. Our process also uses reinforced learning with human feedback (RLHF). This human-in-the-loop technique optimizes an AI systems learning by giving it direct feedback.
With our expertise you can launch your AI model with confidence.
Frequently Asked Questions
What is Supervised Fine-Tuning (SFT)?
Supervised Fine-Tuning is the process of improving a base language model by training it on carefully curated datasets with known outputs. We provide high-quality, domain-specific data that enables SFT to align your models more closely with your desired behavior—boosting accuracy, task performance, and contextual relevance.
What is Prompt Engineering?
Prompt Engineering involves designing and refining prompts to guide large language models toward producing optimal responses. We help create and test diverse prompt datasets to ensure your model performs consistently across use cases—maximizing relevance, minimizing ambiguity, and reducing token waste.
What is Reinforcement Learning with Human Feedback (RLHF)?
RLHF is a training method where human feedback is used to guide a model’s learning process after initial fine-tuning. We build and manage RLHF pipelines that incorporate structured evaluations by real annotators—helping your models learn preferred behaviors and reduce harmful or biased outputs.
What are Risk Mitigation Services for GenAI?
Risk Mitigation Services help identify and reduce potential issues in your AI models before deployment. We provide data-driven insights and human evaluations to catch hallucinations, toxicity, bias, and factual errors—ensuring your GenAI system is reliable, safe, and aligned with ethical standards.
