Part IV: Training & Adapting
High quality training data is the single most important ingredient for building effective language models and ML systems. Yet acquiring labeled data through traditional human annotation is slow, expensive, and difficult to scale. Synthetic data generation, powered by LLMs, has emerged as a transformative approach that can produce diverse, task-specific datasets at a fraction of the cost and time required for manual collection.
This module covers the full lifecycle of synthetic data: from foundational principles and generation pipelines through quality assurance, LLM-assisted labeling, and weak supervision. You will learn how to use LLMs as simulators to generate realistic user interactions, build automated red-teaming datasets, create evaluation harnesses, and construct preference pairs for reinforcement learning from human feedback (RLHF). Equally important, you will learn the risks: model collapse from training on synthetic outputs, bias amplification, and data contamination.
By the end of this module, you will be able to design end-to-end data generation pipelines, implement quality filtering and deduplication strategies, combine LLM labels with human oversight through active learning, and apply weak supervision to create large labeled datasets programmatically. These skills form the essential foundation for the fine-tuning modules that follow.