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Book Overview & Buying
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Table Of Contents
LLMs in Enterprise
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Training and fine-tuning LLMs requires more than just access to data. It demands a well-defined data strategy. This strategy guides how data is sourced, processed, and optimized across different stages of the model development life cycle, particularly during pre-training and fine-tuning, ensuring that every component in the pipeline, from raw data collection to dataset curation and augmentation, aligns with the model’s objectives, contributing to a high-performance outcome. This strategy requires a careful balance between quality and quantity, domain relevance, and ethical considerations while minimizing inefficiencies and preventing biases. Whether developing an LLM from scratch or tailoring a pre-trained model for specific use cases, a strong data strategy serves as the foundation for efficiency, reliability, and scalability.
But why, exactly, is data at the heart of the life cycle of LLM development...