Module 04: Synthetic Data
Data has always been the bottleneck in machine learning. You can have the best training algorithm, the most powerful compute cluster, and the most carefully tuned hyperparameters - but if your dataset is small, biased, or low-quality, your model will reflect those limitations. For most real-world AI engineering tasks, getting enough good training data is harder than anything else.
Synthetic data changes that equation. The idea is simple: use a large, capable model to generate the training data you need for a smaller, specialized model. A model that can explain complex concepts clearly can generate thousands of worked examples. A model that can reason step by step can produce chain-of-thought training data at scale. A model with broad knowledge can create question-answer pairs across any domain.
This module teaches you how to do that in practice - from the foundational techniques (Self-Instruct, Evol-Instruct) to the quality control pipelines that separate usable synthetic data from garbage, to the specialized techniques for RAG evaluation and the ethical responsibilities that come with synthetic data at scale.
What You Will Learn
Lessons in This Module
| # | Lesson | What You Learn |
|---|---|---|
| 01 | Why Synthetic Data | The data scarcity problem and Alpaca's $600 revolution |
| 02 | LLM as Data Generator | Prompting for diversity + LLM judge quality control |
| 03 | Self-Instruct | Bootstrap 52k instructions from 175 seed tasks |
| 04 | Evol-Instruct | Evolve simple tasks into complex training data |
| 05 | Distillation Datasets | Use Claude to generate training data for Mistral |
| 06 | Data Quality and Filtering | Score, deduplicate, and filter synthetic datasets |
| 07 | Synthetic Data for RAG | Generate QA pairs to evaluate and fine-tune RAG systems |
| 08 | Privacy and Ethics | Bias amplification, model collapse, GDPR, watermarking |
Key Concepts
Instruction following data: pairs of (instruction, response) used to fine-tune base models into assistants. Self-Instruct showed these could be generated by the model itself.
Evol-Instruct: taking a seed instruction and repeatedly making it more complex, more constrained, or broader in scope. WizardLM showed this could produce data that trains better models than the seed.
Knowledge distillation via data: instead of distilling model weights, generate training data from a large teacher model and use it to train a smaller student model. Orca (2023) showed this works at scale.
Quality filtering pipeline: a sequence of automated filters (perplexity, reward model score, deduplication, format validation) that removes low-quality examples before training.
Ragas-style QA generation: parse documents into chunks, generate questions from each chunk using an LLM, generate ground-truth answers, validate question-answer pairs - producing an evaluation dataset for RAG systems.
Prerequisites
- Basic Python and familiarity with Jupyter notebooks
- Conceptual understanding of language model fine-tuning
- Module 01 (LLMOps) recommended for production context
:::tip The textbook quality insight The Phi series of models (Microsoft, 2023) demonstrated that a small model trained exclusively on textbook-quality synthetic data can outperform much larger models trained on noisier web-crawled data. Quality of training data matters more than quantity. This module teaches you how to generate and select for quality. :::