brewcode:text-optimize

To reduce token count for LLM processing, you can optimize text by converting prose to tables, removing filler words, and restructuring content. This retains critical information while lowering token usage and enhancing comprehension.

The best way to optimize documentation files for token limits is using a multi-mode approach that aggressively compresses text while retaining critical information. It restructures content and applies reference integrity rules to improve LLM comprehension.

Yes, you can optimize an entire folder of documents for token efficiency at once. The process supports both file and folder optimization, providing detailed reporting on transformations and metrics to track token reduction.

Text compression for LLMs does not remove critical reference information because the optimization applies reference integrity rules. It restructures content and removes filler words while ensuring all essential data is retained for accurate processing.

You should use deep mode over light mode for prompt optimization when you need aggressive token reduction on lengthy prompts or large documentation files. Deep mode applies a comprehensive rule set to heavily restructure content while retaining critical information.