Synthetic Data

- Intro to Synthetic Data and Why It Matters in Modern AI - What Synthetic Data Really Is vs Common Misconceptions - How Synthetic Data Fills Gaps When Real Data Is Limited or Unsafe - The Synthetic Data Flywheel: Generate → Evaluate → Iterate - Using Synthetic Data Across Pretraining, Finetuning, and Evaluation - Synthetic Data for RAG: How It Stress-Tests Retrieval Systems - Fine-Tuning with Synthetic Examples to Update Model Behavior - When to Use RAG vs Fine-Tuning for Changing Information - Building RAG Systems Like Lego: LLM + Vector DB + Retrieval - How Vector Databases Reduce Hallucinations and Improve Accuracy - Generating Edge Cases, Adversarial Queries, and Hard Negatives - Control Knobs for Diversity: Intent, Persona, Difficulty, Style - Guardrails and Bias Control Using Prompt Engineering and DPO - Privacy Engineering with Synthetic Data for Safe Testing - Debugging AI Apps Using Synthetic Data Like a Developer Debugs Code - LLM-as-Judge for Fast, Cheap, Scalable Data Quality Checks - Axial Coding: Turning Model Failures Into Actionable Error Clusters - Evaluation-First Loops: The Only Way to Improve Synthetic Data Quality - Components of High-Quality Prompts for Synthetic Data Generation - User Query Generators for Realistic Customer Support Scenarios - Chatbot Response Generators for Complete and Partial Solutions - Error Analysis to Catch Hallucinations, Bias, and Structure Failures - Human + LLM Evaluation: Combining Experts With Automated Judges - Model Cards and Benchmarks for Understanding Model Capabilities