prompt-compressor

To reduce token usage in long LLM prompts, you can compress verbose text by identifying core intent, extracting essential context, and applying token abbreviations before processing. This removes redundant phrasing and filler while preserving the original meaning.

The best way to compress text for an LLM is to apply a compression utility that trims unnecessary language and filler from technical docs and conversations. This focuses the prompt on the core intent while retaining essential context for accurate responses.

Prompt compression works effectively on pasted user content and transcripts by trimming unnecessary language and hedging. It processes inputs longer than 1500 tokens to achieve noticeable savings in token counts while keeping the core intent intact.

You can choose a compression level for your prompt text by selecting light, medium, or heavy options based on your token reduction needs. After providing the text, review the compressed result and token savings to ensure the essential context is preserved.

You should apply token abbreviations to technical documentation when your inputs exceed 1500 tokens or contain redundant phrasing. This removes fluff and focuses the prompt on the essential context needed for accurate LLM processing.

You can protect specific text from being removed during prompt compression by defining protected content within your inputs. The compression process preserves this protected content while applying token abbreviations and removing fluff from the remaining text.