Short experiments often end with a noisy dashboard and a debate: was that a real win, a bot spike, or a cache artifact? This conversational 15‑minute panel shows a practical, low‑engineering way to use large language models as a decision‑support layer — not an oracle. Gustavo (measurement) explains how to export, sanitize, and structure experiment CSVs so they’re safe to share with a model; Michelle (brand) shows prompt templates that turn raw metrics into concise postmortems, narrative summaries for stakeholders, and prioritized next hypotheses that respect brand guardrails. We close with an explicit 72‑hour runbook: how to run the export → sanitize → LLM→human‑review loop, minimum checks to trust suggestions, and a subscribe CTA to grab the prompt pack and safe‑data recipes. Listeners leave able to turn confusing short‑run results into clear, actionable follow ups without leaking PII or outsourcing judgment.