Forecasting is crucial for decision-making, but numbers often lack context. Imagine predicting sales without knowing a major holiday is coming – your forecast would be way off. That's where the "Context is Key" (CiK) benchmark comes in. It tests how well forecasting models use textual information, like knowing about holidays, product launches, or economic downturns, to make accurate predictions. The benchmark uses real-world data, from solar energy production to unemployment rates, paired with descriptive text that’s *essential* for good forecasts. Researchers tested various approaches, from traditional statistical models to cutting-edge AI, and found that large language models (LLMs), especially when prompted directly for a forecast, performed remarkably well. One prompting method, called "Direct Prompt," even outperformed specialized time-series models when used with a massive LLM like Llama 3.1. This shows the power of LLMs to understand and apply complex information. While LLMs show promise, the research also reveals their limitations. They can struggle with specific formats like scientific notation and are computationally expensive. The future of forecasting lies in multimodal models that can efficiently combine numbers and text. Imagine AI assistants that can incorporate your expertise and automatically gather relevant information to generate even more accurate, context-rich predictions.