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.
import llmlingua compressor = llmlingua.PromptCompressor()
compressed_prompt = compressor.compress_prompt( prompt=large_log_prompt, rate=0.4, # Keep 40% of tokens condition_compare=True )
out = call_llm(model="gpt-4o", prompt=compressed_prompt)
Claude follows compressed prompts well. Ensure core XML tags (, ) remain intact so formatting logic is preserved.