semantic-compress

To shrink LLM prompts while preserving behavior, replace standard conceptual explanations with minimal pointers and validate the compressed output against a defined transfer set of test cases to ensure identical model behavior.

The best way to reduce token bloat in a verbose CLAUDE.md file is to use A/B-validated distillation loops that produce the smallest possible document while maintaining identical behavioral output and enforcing all project rules.

A/B-validated distillation works by iteratively compressing LLM-directed documents and testing the reduced output against a transfer set of test cases to verify it induces the exact same model behavior as the original.

Yes, you need a defined transfer set of test cases to verify that the compressed system prompt induces the same model behavior as the original document and preserves all bespoke project constraints.

Yes, you can execute a rapid local mode core-to-pointer pass for short snippets that replaces standard conceptual definitions with concise cues, ensuring your instruction set remains efficient.

The main limitation is the requirement for a defined transfer set of test cases; without these test cases to verify behavioral equivalence, the compression cannot guarantee the model will adhere to bespoke project constraints.