Synthetic data for LLMs can be generated using multiple techniques, each with its own strengths:

1. LLM-Generated Synthetic Text: Pre-trained LLMs (e.g., GPT-4, Llama 3, Mistral) generate contextual text, dialogues, and instruction-following examples. For example: “Generate 1,000 customer service inquiries along with their AI-generated responses.”
2. Retrieval-Augmented Generation (RAG): Combines real-world knowledge retrieval with synthetic data generation, improving factual consistency. Used for tasks like question answering, summarization, and document processing.
3. Self-Play & Adversarial Generation: Involves multiple AI agents interacting with each other, creating synthetic conversations, debates, and role-play datasets. Used for chatbots, negotiation models, and multi-turn dialogue systems.
4. Data Distillation & Augmentation: Fine-tuning smaller models using distilled outputs from larger models to improve efficiency without sacrificing quality. Dria enables this through parallelized inference across multiple LLMs through decentralized nodes.
5. Programmatic Generation (Code-Based Approaches): Structured templates and scripts create synthetic datasets for specific tasks, such as code generation, SQL queries, or structured information extraction. For example: Dria’s Pythonic function calling synthetic dataset improves LLM function execution performance by using Pythonic syntax instead of JSON.