ai-config-compress

Prompt compression reduces token count by removing filler, deduplicating rules, and normalizing system messages. It preserves behavioral intent through tiered structural and semantic compression, outputting diffs and token savings while keeping the original instructions functionally intact.

The best way to reduce token count in long AI instruction sets is applying tiered prompt compression. This process applies mechanical, structural, semantic, and aggressive compression techniques to streamline guidelines while visualizing risk trade-offs and validating the final output.

You can aggressively compress coding task prompts using Tier 4 aggressive compression, but it carries higher risk. The workflow provides risk notes and preservation trade-offs for each tier, allowing you to validate the final compressed output through testing before deployment.

Use semantic compression over mechanical deduplication when structural edits and filler removal are insufficient for your token budget. Semantic compression alters phrasing to streamline prompts while preserving intent, offering deeper token savings with visualized risk trade-offs.

Prompt compression works for system messages, standard user prompts, rules, and guidelines across AI workflows. The tiered workflow applies deduplication and normalization to any LLM instruction set, outputting a final compressed version validated by testing.