Filter, Then Prompt: How Context Compression Saves Tokens While Boosting Accuracy

As RAG systems become standard for LLM applications, context compression has emerged as a critical optimization layer. Passing entire retrieved chunks to the LLM wastes tokens and paradoxically hurts accuracy—Stanford's 'Lost in the Middle' research confirms that longer contexts degrade model performance. Extractive compression filters retrieved text before prompting, slashing costs while improving quality, making it essential knowledge for teams building production RAG systems.