LLMSlim Library Claims to Cut RAG Prompt Token Usage by Over 50%

A developer has released LLMSlim, an open-source Python library designed to compress large language model prompts without discarding critical information. The tool targets a common inefficiency in retrieval-augmented generation (RAG) pipelines, where retrieved documents often contain filler text that consumes a significant share of the token budget. Beyond cost, excess tokens create performance problems: transformer attention scales quadratically with sequence length, and LLMs tend to underweight information buried in the middle of long contexts. LLMSlim uses a six-step deterministic pipeline combining TF-IDF scoring, LexRank centrality, and priority-tier locking to protect high-value sentences such as those containing instructions or numerical data. Benchmarks on 500 prompts per dataset report over 50% token reduction at sub-30ms latency while retaining 100% of system directives.