Dataset Engineering for LLMs: Data Curation, Synthetic Data & Quality | Chapter 8
About this lesson
Download the source code from here: https://onepagecode.substack.com/ Dataset engineering is one of the most important yet under-discussed parts of building high-performing LLMs. In this chapter, we cover how to curate, generate, and process high-quality training data for finetuning and post-training. Data quality, coverage, and quantity directly impact your model’s performance. This chapter gives you a practical framework for building datasets that actually work. What you’ll learn: • The three pillars of dataset engineering: Quality, Coverage, and Quantity • Data-centric AI vs Model-centric AI • How to evaluate and improve data quality • Data synthesis techniques (rule-based, simulation, and AI-powered) • Instruction data synthesis for supervised finetuning • Reverse instruction and self-improvement techniques • Data verification and filtering methods • Model distillation using synthetic data • Practical data processing steps (deduplication, cleaning, formatting) • Building a data flywheel with real user data Whether you're creating instruction data, preference data, or scaling your dataset using synthetic methods, this chapter will help you do it systematically. High-quality data is often the real differentiator between good and great models. Drop a comment: What’s your biggest challenge when it comes to creating training data? #DatasetEngineering #SyntheticData #LLMTrainingData #DataCuration #InstructionData #DataQuality #ModelDistillation #LLM #FineTuningData #Chapter8
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