Generative AI models excel in general tasks, but to enhance their effectiveness for specific use cases, especially in specialized fields, fine-tuning is essential. This process enables an open source large language model to better understand domain-specific knowledge. Rather than simply providing behavioral examples, users can integrate relevant information directly into the model, resulting in improved responses, faster performance, and lower computational costs. The video introduces the fine-tuning process using InstructLab, which allows users to contribute to AI model development from their laptops without requiring advanced technical skills. The fine-tuning process consists of three key steps: first, curating domain-specific data; second, creating synthetic data using a local large language model to expand the dataset; and third, integrating this tailored data back into the model through a technique called Laura. The initial step focuses on data curation within InstructLab, where users can set up their environment and organize data in a hierarchical structure. This structured approach aids in developing a more knowledgeable AI model that aligns closely with user needs and specific industry standards.