Learning about data modeling principles and techniques

Data modeling techniques don’t just help you with technology choices and frameworks – they also enable you to prepare the premises for the industry-specific use case that you are going to address with the dataset. Some data modeling techniques and principles can be effective in maximizing the potential of using LLMs in such applications while modeling the data for them:

• Data quality and preprocessing: Ensure data quality by performing rigorous preprocessing steps, including data cleaning, normalization, and deduplication. High-quality data improves the performance and reliability of LLMs and prevents them from learning spurious patterns.
• Fine-tuning: Leverage pre-trained LLMs as a starting point and fine-tune them on domain-specific or task-specific data. Fine-tuning allows the model to adapt and specialize for specific applications, reducing the need for extensive training from scratch.
• Data augmentation...