This post discusses the performance of Claude 2.1, an LLM model, in recalling facts at different document depths. The findings indicate that facts at the top and bottom of the document were recalled with high accuracy, while performance decreased towards the middle. It is suggested to experiment with prompts and conduct A/B tests to improve retrieval accuracy, not to assume guaranteed retrieval of facts, reduce context length for better accuracy, and consider the position of facts within the document. The test aimed to gain insights into LLM performance and transfer that knowledge to practical use cases.