I originally tackled this problem of combinatorial explosions when generating synthetic data and then discovered the LLMs couldn't help me - Let me break down why generating a million rows of synthetic user data for Midtown NYC residents is challenging for an LLM: 1. Resource and Context Window Limitations: - LLMs typically have limits on how much text they can generate in one response - Generating a million records would require numerous API calls or responses - Each row needs to be unique and contextually valid, which requires maintaining coherence across a massive dataset 2. Data Consistency Challenges: - Need to maintain realistic patterns in: - Address distributions across Midtown's actual street grid - Age demographics matching the neighborhood - Income levels appropriate for the area - Household sizes typical for Midtown apartments - Names that reflect NYC's diverse population 3. Internal Correlation Requirements: - Data points need to make sense together - Example: A person's job should correlate sensibly with their income and age - Apartment numbers should match real building structures - ZIP codes must be accurate for Midtown locations For your class project, I'd recommend instead: 1. Generate a smaller sample (e.g., 1000 records) using an LLM for the structure 2. Use traditional programming tools (Python, R, etc.) to scale it up with controlled randomization 3. Validate against real NYC demographic data Would you like me to help you create a small template of realistic records that you could then scale up programmatically? Its not the LLMs's fault - they are next token prediction not next token generators. You can see another implementation of the code base done early on on the WatsonAI Web site - its a wide spread problem that needs a better mousetrap.

SDG Synthetic Data Generator

Never Run Out of Text Based Synthetic Data Ever

Never Run Out of Text Based Synthetic Data Ever