What are the trade-offs of using LLMs in practical applications?

Using LLMs in practical applications comes with several trade-offs including computational cost, interpretability, and responsiveness. LLMs require significant computational resources for both training and inference, which can be costly and limit deployment in resource-constrained environments. Additionally, their outputs can be complex and difficult to interpret, hindering user trust. While LLMs often produce high-quality responses, they can also generate irrelevant or incorrect information, necessitating effective methods for user oversight or filtering. Balancing performance, cost, and reliability is key to successful LLM deployment.