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Fine-tuning LLMs for specific domains



LLMs (Large Language Models) are a specific type of AI model that has gained prominence for their ability to process and generate human-like language. These models are built by training on massive amounts of text data and can perform tasks like text generation, translation, and question-answering.

One technique used to enhance the performance of LLMs is fine-tuning. Fine-tuning involves adjusting a pre-trained language model to adapt it to specific tasks or domains. This process bypasses the need to train the model from scratch and instead focuses on training it with additional task-specific data. This allows for the customization and specialization of the model to improve its performance in particular applications, such as translating, summarizing text, or answering specific questions.

Fine-tuning is a crucial aspect of transfer learning, a technique where knowledge learned from one task is applied to another related task. By leveraging the pre-trained model's understanding of language and fine-tuning it with task-specific data, LLMs can effectively bridge the gap between general language understanding and task-specific performance.

Enterprise Generation AI (GenAI) solutions have gained significant traction among organizations seeking to unlock the potential of their private data repositories. These solutions offer a secure and controlled environment for developing models using proprietary data.

LLM training for companies


The company is experiencing consistent growth in its customer base, leading to a surge in technical support and customer service inquiries. The support team is overwhelmed by the volume of queries, which is hindering their ability to promptly and accurately respond to customer questions. Moreover, many customers pose detailed technical inquiries about the company's products and services, requiring a deep understanding of technical terminology. The company had its own repositories with all the information but they were unable to retrieve that information and use it in a meaningful way.

Pangeanic's solution

Fine-tune a pre-trained Large Language Model with the company assets to address these challenges.

At Pangeanic we created a tailor-made model to tackle this specific task, offering the company numerous advantages over general-purpose LLMs. By training on task-specific data, custom LLMs excel in performing tasks that require domain-specific knowledge and understanding. Moreover, by using the company’s own datasets we were able to create an accurate and reliable custom Large Language Model that addressed the unique challenges that they were facing.



How does it work?

We adapt the models to various contexts where there is extensive documentation and we want the model to be able to answer a variety of questions. Specifically in this realm, LLMs prove highly beneficial for developing customer service chatbots or implementing internal chatbots within a company.

Compared to general LLMs, which are designed to handle a broad range of tasks, custom LLMs demonstrate improved accuracy and efficiency when dealing with tasks that align with their specialized training. This is because they have been fine-tuned to comprehend and generate language within the specific context of the task or domain.

The result is a powerful tool that can enhance productivity and deliver precise outcomes in diverse field, as in this case, technical support and customer service inquiries, showcasing the potential of AI to revolutionize various industries.

In this case, we are talking about the implementation of an LLM fed with all the company's procedural manuals, covering areas such as systems, technical incidents and product manuals, among others. Through this learning process, the model can generate answers to external questions and avoid message overload on the technical support team, such as: "What is the procedure for hardware repair?" or "What is the emergency telephone number?" and many other similar queries. This approach could revolutionize the way companies manage information and external communication.

Enterprise GenAI streamlines the process of building and managing AI models that are tailored to specific organizational requirements.

With this solution we can create accurate and reliable models that address the specific needs of enterprises by leveraging fine-tuning techniques and private datasets.

Nos gusta la filosofía de trabajo de Pangeanic así como su profesionalidad. Su constante trabajo de escucha activa hacia el cliente les hace mejorar cada día más y esa, bajo mi punto de vista, es una de sus grandes cualidades.
Rafael de Jorge
Director de Marketing - Onahotels
Servicio rápido y eficiente. Encontramos la creación de una base de datos de traducciones realizadas por parte de Pangeanic una herramienta muy útil para otras ocasiones y/o trabajos similares.
Chloe Wu
La calidad es excelente, como de costumbre. El texto de origen cambió muchas veces durante el proceso de traducción. Pangeanic reaccionó rápidamente a los cambios, lo cual nos ayudó mucho.
Eisuke Seki
ES Japan

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