LLMSlim: Deterministic Prompt Compression for RAG Pipelines

Short summary

RAG pipelines waste up to 60% of token budgets on filler prose that exists for human readability, not model comprehension. The author built LLMSlim, a Python library using TF-IDF and LexRank centrality scoring to deterministically compress prompts by 50-65% while preserving 100% of system directives. Key insight: transformer attention scales quadratically, so 2,000 filler tokens on a 4,000-token context increases prefill cost by 225%, not 50%.

• •RAG contexts waste ~60% of tokens on human-oriented filler prose
• •LLMSlim uses TF-IDF + LexRank for deterministic 50-65% compression with 100% directive retention
• •Quadratic attention means filler tokens cost more than proportional — 2k extra tokens on 4k context = 225% more prefill compute

Generated with AI, which can make mistakes.