Optimizing LLM Output Efficiency Through Historical Telegraphese Compression Techniques

Instructing LLMs to use telegraph-era 'cablese' compression reduces output tokens by 40-49% without sacrificing information fidelity, offering significant API cost savings. The technique works across multiple model families, though models with mandatory reasoning (e.g., GPT-5-mini) face performance trade-offs.

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