prompt caching is the most bang for buck optimisation you can do for your LLM based workflows and agents. in this post, i cover tips to hit the prompt cache more consistently and how it works under the hood (probably the first such resource) https://t.co/0zi6sBCvU2 https://t.co/woCzOn02at

This pattern (set up, do work, clean up) is common across many different problem domains, so Python has a language feature to help with it. Context managers, defined using the with keyword, manage the setup and cleanup processes. Python's file objects support them, as do most database libraries.

Anticipate Failure Anything that could improve connectivity is a positive thing to look forward to. The key is keeping client latency low and ensuring clients are prepared for very high traffic with cache control, sidecars, timeouts, circuit breakers, and retries. In addition to improving healthy signals from the services, it's critical to... See more

Here are some rules of thumb for knowing when to use try/catch :

• Do you control the input?
  
  
  If the variable in question is coming from a user, an API, or some other external source, you should probably wrap its initial handling in a try/catch block.


• Is the error recoverable?
  
  

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