Prompting and In-Context Learning: Optimizing Prompts for Mistral Large

Abstract The exploration of the synergy between prompting and in-context learning reveals significant improvements in the performance of language models when tailored instructions and relevant context are integrated. The research delves into various prompt designs, assessing their impact on tasks such as text summarisation, machine translation, and question-answering. Prompts that include clear, explicit instructions and contextual information significantly enhance model outputs in terms of accuracy, coherence, and relevance. Experiments with the Mistral Large model demonstrate that adaptive prompting, which dynamically adjusts based on real-time interactions, can further refine model performance. Challenges such as balancing the amount of context to avoid information overload and the sensitivity of model responses to subtle changes in prompt phrasing are addressed. The study's findings underscore the critical role of effective prompt engineering and contextual integration in maximising the potential of language models. Future research directions include developing systematic methods for prompt design, optimising contextual information, and exploring cross-task generalisation. This research contributes valuable insights into guiding and informing language models, paving the way for more intelligent and adaptive AI systems across diverse applications.