Lookahead bias cannot be measured with a model that has it. Researchers studying how beliefs formed, how markets read events, or how narratives spread need a reference point: what was actually known at a given date. A standard model can be prompted to adopt a historical perspective, but its weights contain everything written since. It imitates the perspective. It cannot reconstruct it.

Ask every vintage the same question

Each vintage is a version of the model trained only on text available up to its date, and every vintage in the catalog is addressable by date. A single call can run one prompt against a range of them. Ask a vintage from before a market break how risky an asset class is, then ask a vintage from after it. The answers differ, and the difference is the lookahead bias on that question, made visible and measurable.

In practice this works as a control next to whatever model you already run. The same prompt goes to your production model and to the matched vintage, and the gap between the answers shows what hindsight contributed. For event studies and narrative research, the vintage answer serves as a baseline of contemporaneous views, reconstructed from the text of the period.

Vintage answers reflect the text of their time, including its errors and its consensus views. By design they are records of what was known and said at the time, not judgments made after the fact.