Training & Data

Data labeling is the process of annotating data with meaningful tags or labels, which is essential for supervised learning as it enables models to learn the relationship between input data and the correct output.

Fine-tuning is the process of further training a pre-trained LLM on a specific dataset to adapt it to a particular task or domain. It is necessary when the base model does not perform adequately on specialized tasks or data.

Unstructured data refers to information that does not have a predefined data model or organization, such as text, images, audio, and video. GenAI models are particularly effective at processing unstructured data.

Semi-structured data have some organizational properties but do not conform to a rigid structure, such as JSON, XML, or log files. They are more flexible than structured data but easier to process than unstructured data.

Structured data is highly organized and formatted in a way that is easily searchable, such as in relational databases and spreadsheets. It is typically used in traditional analytics and business intelligence applications.