Imagine trying to understand how a brilliant chef creates their signature dish. You can taste the flavors, but you don't know the exact recipe or the source of each ingredient. That's the challenge with today's large language models (LLMs). We know they're powerful, but understanding the origins of their knowledge is a mystery. Now, researchers are using a clever technique called "in-context learning" to shine a light on this black box. Like giving the chef a few key ingredients and observing how they use them, in-context learning feeds specific information to the LLM and analyzes its response. This reveals how different training datasets contribute to the LLM's output, much like identifying the distinct flavors in that complex dish. Two novel methods are at play: one compares the LLM's answers with and without context, while the other uses a mixture model to break down the LLM's knowledge sources. The results show that this approach can successfully pinpoint the datasets that most influence an LLM's responses. This is crucial for understanding why LLMs might excel in certain areas but struggle in others. It also opens doors for improving LLM training, ensuring fairness in data compensation, and even identifying potential biases lurking within the data. While this research is preliminary, it's a big step toward making AI more transparent and accountable. It's like finally getting a glimpse into the chef's secret recipe book, revealing the ingredients and techniques behind their culinary magic. Future research will involve training LLMs on specific datasets to validate these findings and refine the process, ultimately making AI's inner workings less of a mystery.