Prompt Compressors & LLMLingua

Expert

Compress prompts to remove redundant tokens while maintaining task performance.

01 The Concept

Prompts can be compressed to save cost and reduce latency. Algorithms like LLMLingua use smaller language models (like LLaMA-7B or GPT-2) to compute token perplexity.

By dropping low-perplexity tokens (grammatical filler, redundant adjectives, predictable words), these tools can compress prompts by up to 50-80% without losing quality.

02 Weak vs. Strong

EX 01Prompt Compression Pipeline

import llmlingua compressor = llmlingua.PromptCompressor() # Compress log files compressed_prompt = compressor.compress_prompt( prompt=large_log_prompt, rate=0.4, # Keep 40% of tokens condition_compare=True ) # Route to OpenAI/Anthropic API out = call_llm(model="gpt-4o", prompt=compressed_prompt)

→ Why it works

Systematically filters out noisy database metadata and redundant grammar, passing only high-value logs to the LLM.

03 Key Points

01Perplexity Filtering: Analyzing token probability to identify and drop redundant filler words.

02LLMLingua: Open-source project that utilizes smaller models to optimize prompt size before API routing.

03Grammar Pruning: Dropping articles and prepositions that do not alter the core instruction meaning.

04Latency Mitigation: Compressed prompts significantly reduce time-to-first-token (TTFT) and bandwidth.

05Cost Control: Reduces high-volume prompt input costs by up to 80% without modifying backend models.

04 Model-Specific Notes

Claude follows compressed prompts well. Ensure core XML tags (<rules>, <context>) remain intact so formatting logic is preserved.

05 For Your Role

It's like writing a telegram: you remove 'the', 'a', and 'is' to save money while ensuring the recipient still understands the news.

Next: Programmatic Pipelines (DSPy) →