Python

With the advent of large language models (LLMs), asynchronous programming has become a critical skill in AI application development. When working with these computationally intensive models, developers face two key challenges: LLMs are typically served via network endpoints, introducing significant latency that can severely impact application responsiveness. This is where asynchronous programming shine. Unlike traditional synchronous programming, where operations execute sequentially and block until completion, asynchronous programming allows applications to continue processing CPU-bound operations while waiting for I/O-bound responses in the background. This approach not only maximuizes computational resource utilization but also dramatically improves user experiences by keep applications responsive, even when interacting with resource-intensive services like LLMs. ...