top of page

Training Methods for LLMs at GenAISafety

At GenAISafety, the training of large language models (LLMs) is based on advanced techniques designed to ensure optimal performance in risk management related to workplace safety, environment, and health. Here’s an overview of the training methods we use to develop LLMs tailored to the specific needs of our clients:

1. Pre-training on Specialized HSE Data

At GenAISafety, we leverage the innovative Preventera HSE DataHub platform to revolutionize health, safety, and environmental (HSE) risk management. Our solution uses artificial intelligence (AI) to centralize, organize, and analyze vast datasets from multiple sources, while ensuring compliance with standards such as ISO 45001 and ISO 31000

2. Supervised and Semi-Supervised Training

We apply a hybrid approach of supervised and semi-supervised training, combining:

  • Supervised training: Models are trained using annotated examples to ensure high accuracy in specific scenarios, such as risk identification and accident prevention.

  • Semi-supervised training: We utilize partially annotated datasets to enable models to gain flexibility in analyzing less structured data, while maintaining consistency in their predictions.

3. Reinforcement Learning

We also use reinforcement learning, where models receive feedback on their performance and adjust their predictions accordingly. This method allows optimization of results in complex environments, taking into account changing regulations and working conditions.

4. Fine-tuning on Client-Specific Data

We tailor our LLMs to the specific needs of each organization through a fine-tuning phase on company-specific data. This process improves the relevance of the model's responses and ensures that the proposed solutions are aligned with the organization's internal safety policies.

5. Integration of Retrieval-Augmented Generation (RAG)

At GenAISafety, we integrate Retrieval-Augmented Generation (RAG) into our language models (LLMs) to enhance the accuracy and relevance of the generated responses. This approach combines real-time information retrieval from internal or external knowledge bases with AI generation, allowing the model's contextual responses to be enriched. Here’s how we effectively implement RAG:

6. Security and Ethics

Our models are trained with a focus on security and ethics, including:

  • Reduction of algorithmic biases

  • Respect for data privacy (compliance with regulations such as GDPR)

  • Transparency and explainability of decisions made by the models

bottom of page