This paper investigates the prompt-dependent nature of in-context recall in Large Language Models (LLMs), highlighting how a model's ability to retrieve information from its prompt is influenced by the prompt's content and structure. The study uses the "needle-in-a-haystack" method, where a factoid (the "needle") is embedded within a block of filler text (the "haystack"), and the model is asked to retrieve the needle. The research evaluates the recall performance of nine LLMs across varying haystack lengths and needle placements to identify patterns in their recall ability. The findings show that LLM recall is not only dependent on the prompt's content but also on the model's training data and architecture. Models trained on data that conflicts with the prompt's information may perform worse, while adjustments to model architecture, training strategy, or fine-tuning can improve recall. The study also demonstrates that larger models, such as Llama 2 70B, generally perform better in recall tasks, but the benefits of increasing model size diminish beyond a certain point. Furthermore, the research reveals that the placement of the needle within the haystack significantly affects recall performance. Models may struggle to retrieve information placed near the beginning or end of the text, and the effectiveness of recall can be impacted by the model's ability to process and retain information from prompts, especially those exceeding 1k tokens. The study also highlights the importance of evaluating LLMs in various contexts to understand their strengths and weaknesses. The results suggest that in-context recall is a critical factor in determining the effectiveness of LLMs in real-world applications, and that continued research is needed to improve their robustness and adaptability to different tasks. The findings provide insights into how LLMs can be optimized for better performance in real-world scenarios.This paper investigates the prompt-dependent nature of in-context recall in Large Language Models (LLMs), highlighting how a model's ability to retrieve information from its prompt is influenced by the prompt's content and structure. The study uses the "needle-in-a-haystack" method, where a factoid (the "needle") is embedded within a block of filler text (the "haystack"), and the model is asked to retrieve the needle. The research evaluates the recall performance of nine LLMs across varying haystack lengths and needle placements to identify patterns in their recall ability. The findings show that LLM recall is not only dependent on the prompt's content but also on the model's training data and architecture. Models trained on data that conflicts with the prompt's information may perform worse, while adjustments to model architecture, training strategy, or fine-tuning can improve recall. The study also demonstrates that larger models, such as Llama 2 70B, generally perform better in recall tasks, but the benefits of increasing model size diminish beyond a certain point. Furthermore, the research reveals that the placement of the needle within the haystack significantly affects recall performance. Models may struggle to retrieve information placed near the beginning or end of the text, and the effectiveness of recall can be impacted by the model's ability to process and retain information from prompts, especially those exceeding 1k tokens. The study also highlights the importance of evaluating LLMs in various contexts to understand their strengths and weaknesses. The results suggest that in-context recall is a critical factor in determining the effectiveness of LLMs in real-world applications, and that continued research is needed to improve their robustness and adaptability to different tasks. The findings provide insights into how LLMs can be optimized for better performance in real-world scenarios.