Training an LLM typically happens in two main stages. First, the model undergoes pre-training on massive datasets from diverse sources to learn general language patterns. Next comes post-training, where we adapt the model with high-quality, domain-specific data, applying techniques such as SFT (Supervised Fine-tuning), RLHF (Reinforcement Learning with Human Feedback), Evaluation, and red-teaming to ensure the model meets accuracy, safety, and business-specific requirements.

SFT is a training process that using domain-specific, labeled datasets to fine-tuning a pre-trained Large Language Model (LLM) to help the model learn task-specific behavior. Meanwhile, the RLHF method means ranking and refining the model’s responses based on human judgments of quality, safety, and usefulness, making outputs more aligned with human expectations.

RAG (Retrieval-Augmented Generation) connects and retrieves knowledge base outside of its training data sources to answer a user's question. RAG is excellent for adding new knowledge to an LLM, but it doesn't change the model's core behavior. LLM training changes the model's fundamental behavior, tone, and ability to follow specific instructions. Depending on the specific project, we will apply RAG or LLM training to achieve the best result.

The required data volume varies by use case. While foundation models may require billions of tokens, domain-specialized or fine-tuned models can achieve strong performance with smaller, high-quality datasets. LTS GDS specializes in delivering high-quality datasets across the entire training pipeline, including pre-training, SFT, and RLHF.

During the pre-training stage, LLMs are trained on large-scale datasets collected from diverse sources. For post-training and building domain-specific LLMs, the focus shifts to quality, expertise, and project-specific requirements. We leverage client-provided materials, licensed proprietary databases, and data curated by experienced subject matter experts to ensure precision and relevance.

Yes, we train and fine-tune multilingual LLMs across 50+ languages and multimodal (vision-language/audio) models when required, preserving cultural nuance and regional context for better user experience.

We begin by clearly defining project requirements and embedding safeguards to prevent bias while adhering to strict ethical standards. Our diverse team of global experts enhances data diversity while updating guidelines to help identify and mitigate bias, stereotypes, toxic content, and discrimination. All will allow the LLMs to remain fair, safe, and reliable.

We follow industry best practices and global standards, including transparency in data processing, GDPR and ISO compliance, secure pipelines, and human-in-the-loop models, making them not only powerful but also trustworthy and responsible.