Summary
This chapter covered the comprehensive process of handling textual data for LLM applications, starting from the collection of data across diverse sources and progressing to the transformation of this data into a structured format that is conducive to analysis and model training. It further explored the preparation phase, wherein data is imported into parallel programming environments for cleansing and standardization, enhancing its quality for LLM fine-tuning. Lastly, it emphasized the importance of automation in streamlining these processes, ensuring both efficiency and consistency in preparing high-quality datasets for advanced language model applications. In the next chapter, we’ll cover LLM development and fine-tuning.
