This article explores five Python decorators that can be used to optimize LLM-based applications. These decorators leverage libraries like functools, diskcache, tenacity, ratelimit, and magnetic to address common challenges such as caching, network resilience, rate limiting, and structured output binding. The article provides code examples to illustrate how each decorator can be implemented and used to improve the performance and reliability of LLM applications.
This article dives into designing a scalable distributed job scheduling service that can handle millions of tasks. It covers system components, API design, scaling strategies, handling failures, and addressing single points of failure.
The use cases covered in the article include caching, queueing, locking, throttling, session store, and rate limiting.
