A news summary based on the article body explains that in quantitative finance, a robust backtest must be deterministic, but Large Language Models (LLMs) are notorious for hallucinations and stochastic outputs, yielding different interpretations across runs. The author, initially skeptical about LLM-based alpha generation, revisited this belief after being repeatedly asked during interviews at quantamental firms how to integrate LLMs for systematic signals. The article outlines an event-driven strategy pipeline: streaming unstructured alternative data, processing text with an LLM, and executing trades based on semantic output. To backtest historically, one would scrape historical text data, align it with asset price returns, and pass the text through the LLM to generate a structured score (e.g., a sentiment metric from -1 to 1). Standard practice dictates calculating the Information Coefficient and Information Ratio (IC/IR) to verify a statistically significant correlation between LLM-generated signals and forward asset returns; if the IC is zero, there is no alpha.