Building LLMSlim: Architecture Deep-Dive into Deterministic Prompt Compression

Short summary

LLMSlim is a Python library for deterministic prompt compression using a six-stage DAG pipeline: regex-based sentence splitting that respects code fences and URLs, TF-IDF vectorization, LexRank centrality scoring, and a four-tier classification system that protects system instructions and named entities from pruning. It avoids neural embeddings to keep latency under 30ms. The post details the architecture and token budget allocation strategy.

• •Six-stage deterministic DAG compresses prompts without model calls or embeddings, targeting sub-30ms latency
• •Four-tier classification system protects system instructions, schemas, and named entities from being pruned
• •TF-IDF plus LexRank identifies redundant prose while preserving high-information sentences

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