LLMs can be slow, often requiring multiple processing steps that may lead to unacceptable wait times for users. Effective prompt engineering is crucial, as both zero-shot and few-shot learning can yield varying results, sometimes consuming valuable context space. Additionally, prompt injection poses significant risks, allowing users to manipulate outputs and potentially extract sensitive information, highlighting the need for robust safeguards.
