Prompt Compression: Cut Token Costs Without Losing Quality

Prompt compression is a technique for reducing the token count in large language model (LLM) inputs by removing low-information tokens, resulting in cost savings and often improved output quality. This practice can decrease token counts by 20% to 80% while maintaining the semantic integrity necessary for effective model comprehension. Manual methods, such as eliminating redundant instructions and replacing prose with structured lists, can achieve a 20% to 40% reduction without quality loss. Automated approaches, like LLMLingua and Selective Context, can achieve even higher compression ratios with minimal impact on performance, making them ideal for dynamic contexts such as retrieval-augmented generation (RAG) systems. Studies have shown that compression can enhance accuracy by filtering out irrelevant noise, and tools like NeuralTrust's TrustGate ensure consistent application of compression policies across platforms.

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