LLM Context Pruning: A Developer’s Guide to Better RAG and Agentic AI Results

This post introduces Provence and X Provence as lightweight context pruning models for RAG pipelines. It details four common failure modes in long-context systems (Context Clash, Context Confusion, Context Distraction, Context Poisoning) and explains how context pruning addresses these by filtering irrelevant information. Provence is described as a cross-encoder model trained to perform document-level relevance scoring and token-level relevance labeling simultaneously. It can be integrated into existing RAG pipelines with minimal overhead, and its pruning behavior can be tuned. The post also outlines quantitative experiments comparing Provence, X Provence, and OpenSearch Semantic Highlighter on the WikiText-2 dataset.