playground
Watch your prompt shrink.
This is LLM Guardian's actual Semantic Folding engine, compiled to run in your browser — not a mockup. Everything stays on this page; nothing is uploaded.
95
original tok
95
folded tok
0%
smaller
2.7 ms
fold time
We need to refactor the VCM sharder module because the current implementation loads the entire knowledge graph into memory on every request. The refactor should introduce lazy loading with an LRU cache, add unit tests for the eviction path, and benchmark the change against the 10k-memory haystack fixture. Please also update the docs and bump the minor version when we ship it.
entities preserved: 0 · actions: update, refactor, fix, implement, test · semantic density: 0.00 · est. savings: $0.0000/request
What just happened
Semantic Folding distills verbose prose into [ACTION:…][TARGET:…] entity-dense headlinese, then keeps only the sentences that carry information the model can't infer — scoring each sentence for entities, actions, and semantic density, and never touching code blocks. Inside LLM Guardian the same engine runs before every request, alongside VCM Sharding, tool gating, and prompt caching, typically cutting total prompt tokens by 80–95%.