synthetic-data
8 free lessons tagged synthetic-data across AI, Business. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Model Collapse, and the Rule That Avoids It
Training on your own model's output degrades it, and the widely cited result rests on an assumption people rarely notice: that synthetic data replaces real data. Follow-up work showed accumulating synthetic alongside real bounds the error instead. This lesson covers both stages of collapse, why the rule is accumulate rather than replace, how to set a mixing ratio, and the evaluation discipline.
Filtering: The Half That Decides Quality
Generation is the cheap half. What you discard determines what the student learns. This lesson orders the filters by strength: machine verification where an answer can be checked, self-consistency where it cannot, LLM-as-judge with its known position and length biases, and cheap heuristics. It ends on contamination, the failure that invalidates results rather than degrading them.
Generation: Getting Coverage, Not Just Volume
Prompt a model for a thousand examples and you get one example a thousand times, with the nouns changed. This lesson covers why raising temperature does not fix that, the seed-conditioning trick that does, how Self-Instruct and Evol-Instruct systematise it, programmatic generation where ground truth is known by construction, and how to measure whether a dataset actually covers its input space.
When Generating Data Beats Collecting It
Synthetic data is roughly three orders of magnitude cheaper than human annotation, and cheapness is the least interesting thing about it. This lesson establishes what generation can and cannot manufacture: it produces coverage and format, never information the generator lacks, and the one exception is verifiable domains, where a checker turns generation into search.
The Average Trap, Model Collapse, and Responsible Practice
Two failure modes that survive perfect methodology: a prediction objective that pulls research toward the average consumer, and a feedback loop where synthetic data degrades the evidence base. Plus the guidelines for using this responsibly.
The Analytic Flexibility Problem
Silicon samples require dozens of defensible setup choices, and those choices change the answer. The study that generated 252 configurations, found correlations ranging from .23 to .84, and what it means for anyone reporting synthetic results.
Where Synthetic Respondents Break Down
The failure modes that show up when silicon samples are checked against real human data: flattened variance, wrong-signed estimates, portrayal rather than simulation, and the coverage bias that no sample size fixes.
Silicon Samples: Surveying a Model Instead of People
Where the idea of using language models as synthetic survey respondents came from, the three founding results that made marketing take it seriously, and the economics that make it so tempting.