Reliable inflation forecasts play a critical role in economic stability and policy decisions. Traditional econometric models perform well but often overlook qualitative signals that could improve predictive accuracy. Recent advances in AI-based Natural Language Processing enable the extraction of latent sentiment, offering a promising avenue for inflation forecasting. This study proposes a framework that combines Large Language Models (LLMs) to extract sentiment variables from the Brazilian Monetary Policy Committee (COPOM) minutes, optimize bias to match human-collected sentiment, and integrate them into ARIMA and LSTM models for one-step-ahead monthly IPCA prediction. Results show that LLM-generated sentiment trends are temporally coherent with historical inflation patterns and highly statistically significant (p < 0.001). Models whose sentiment evaluations aligned more closely with human assessments (e.g., grok-4-fast and llama-4-maverick) achieved superior forecasting performance. ARIMA models consistently benefited from sentiment inclusion, while LSTM results were more variable.