Synthetic Persona Engineering

Don’t be data poor — Anuj Iravane, Anterior

Aug 19, 2026 · 16:46

Anuj Iravane, who leads AI at Anterior, explains why the company builds synthetic medical data by reversing its inference workflow: the real faxed records it needs most are protected patient data its contracts forbid keeping. With about 70% of medical communication still moving by fax, Anterior starts from a label, samples a reasoning trace from policies modeled explicitly as decision trees, then builds a coarse-to-fine patient journey and documents, making labels correct by construction. Clinicians own the pipeline as interchangeable skills, and roughly 90% of datasets are now synthetic; in a blind review they separated synthetic from real only about 60% of the time. The payoff is creating edge cases on demand and testing workflows before deployment instead of waiting on customer data.

Persona Engineering: A Field Guide to AI Synthetic Personas — Ishan Anand, InsightSciences.ai

Jul 29, 2026 · 21:09

Ishan Anand of InsightSciences argues that synthetic personas, powered by LLMs, can predict human survey responses with 83% alignment when normalized against human noise (humans are only 80% consistent with themselves over time). However, they fail in three critical ways: models invent confounders (e.g., price as a proxy for quality, creating an inverted U-shaped purchase curve), exhibit extreme order bias in answer choices, and predict stated attitudes far better than actual behaviors. Techniques like fine-tuning on human distributions (the subpop paper) or mapping model-generated text to human-scaled responses via semantic similarity can recover accurate distributions, not just averages. To validate, Anand recommends using correlation plus shape metrics and establishing a noise floor by splitting human data against itself. The takeaway: synthetic personas are forecasts, not ground truth, and work best as a complement to human research—extending data to unasked questions and simulating human-plus-agent ecosystems.