LLMs can be sensitive to small changes in the prompt, which can significantly impact the output.

Prompt engineering relies on the model's understanding of the prompt, which can sometimes lack common sense or real$-$world knowledge.

LLMs requires careful consideration of exception handling to ensure that they generate appropriate responses for out$-$of$-$the$-$ordinary or unexpected inputs.

Prompt engineering allows precise fine$-$tuning of language models, enhancing their responsiveness to nuanced prompts and improving overall performance.