Transforming data
Transforming the data into a centralized repository is a critical step in preparing for effective data analysis and model training, particularly with LLMs. Let’s go through the detailed process of organizing, cleaning, and processing data from various sources—structured, semi-structured, and unstructured—into a cohesive repository. Each data type presents unique challenges and requires tailored approaches to ensure its utility in enhancing model performance. We will explore the strategies and methodologies necessary to construct such a repository, laying the groundwork for the data to be readily accessible and in an optimal state for both analysis and the training of sophisticated LLMs.
Defining core data attributes
The data structure definition, referred to as a schema, must encapsulate core attributes that are common across all data types, ensuring they are tailored for a Q&A context. This involves the inclusion of the following:
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