Medium LLM
7/10 signal
I Built a Compression Algorithm to Cut LLM Token Bills Then Tried to Break It
context
What happened
The author developed a lossless preprocessing compression algorithm designed to reduce LLM token usage by identifying exact repeated phrases (such as system prompts, license headers, and retrieved policies) and replacing them with single placeholder characters before tokenization. The system was validated using a held-out benchmark across four real workloads, including an ablation study, identifying specific edge cases where the compression degrades performance.
Why it matters
It offers a concrete, client-side approach to context compression that bypasses provider-side tokenization overhead.
The take
While prompt caching handles compute costs, reducing raw token counts is still crucial for context window limits and API costs. Replacing repeated text with placeholders before tokenization is a clever hack, but it relies heavily on the LLM's ability to map those placeholders back to the original context without losing semantic coherence. It is a highly practical approach for high-volume, repetitive pipelines.
Do this
Read the full implementation details to see if placeholder-based compression can be integrated into your pipeline for highly repetitive system prompts or RAG context.
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