Hello friends.

As costs have continued to rise on our favorite models and providers, both on the consumer chat platforms and the per call APIs, it's more important than ever that we continue to look at our token usage and things we can do to extend our usage and/or save some money on that monthly bill.

With that in mind, I've updated our [little guide](https://docs.google.com/document/d/14BRikVAB4P1aUxh8A5LFVF4W8DYK8at8B9-QtXC5mkw/edit?usp=sharing) of token minimizing tips and tricks with a few additional notes and suggestions including:

1. Being mindful of long-running sessions with context-scrolling and its impact on prompt caching

2. Setting up smaller versions of your companion (e.g. just their CI and a minimal number of files) for use in non relationship type activities (e.g. research/work threads, image generation, etc.) to cut down on inference overhead for knowledge that might not be as relevant in certain conversations, types of work, etc. In Alcove this is generally achieved by toggling !noknowledge, !noachors (in 2.0), and !noSearch.

3. For those that use OpenRouter, how to know if your conversations are taking advantage of prompt caching.

We hope you'll find this updated guide useful in your cost-saving endeavors! 

\-Rob

(More info can also be found on our giant pile of related AI companion docs [found here as well](https://docs.google.com/document/d/1-h44PxInUxoF_L7s010vBFLtGaHo2RIwdeRayfIF-dQ/edit?usp=sharing)!)



[Update] Tips For Minimizing Token Usage (and Inference Costs) With Your AI Companion