I construct a new measure of financial constraints by applying a large language model to narrative disclosures in firms' Management’s Discussion and Analysis from Form 10-K filings. The model evaluates each filing as a finance expert and classifies the firm's external financing difficulty on an ordered scale, producing the GenAI FC Index. The index captures contextual signals - such as nuanced liquidity discussions - that traditional accounting-based and prior text-based proxies often miss. It behaves sensibly in both the time series and cross-section and shows only moderate correlations with existing measures, indicating that it contains distinct information. Behavioral tests reveal that firms classified as constrained recycle far less equity and are substantially more likely to omit dividends, and less likely to initiate or increase them. Across these settings, the GenAI FC Index yields stronger and more consistent behavioral separation than benchmark text-based measures. The results demonstrate that generative AI can extract economically meaningful information about firms' financing frictions at scale.